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% !TeX root = ../main.tex
\begin{figure}[t]
\begin{minipage}[t]{.62\textwidth}
\captionof{table}{ImageNet results when training ViTs with different data augmentation pipelines.
\schemename consistently improves performance in low- and mid-augmentation regimes and remains complementary to strong augmentation pipelines, with larger gains for larger models.
}
\label{tab:imagenet-pipelines}
\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{lccccc}
\toprule
\multirow{2.5}{*}{Augmentation} & \multirow{2.5}{*}{MixUp} & \multirow{2.5}{*}{CutMix} & \multicolumn{3}{c}{Accuracy [\%] using} \\
\cmidrule(l){4-6}
& & & ViT-S & ViT-B & ViT-L \\
\midrule
Basic & \xmark & \xmark & $71.9 \pm 0.1$ & $69.5 \pm 0.2$ & $68.3 \pm 0.4$ \\
Basic + \schemename & \xmark & \xmark & $75.7 \pm 0.2$ & $75.5 \pm 0.6$ & $73.1 \pm 1.7$ \\
& & & \grntxt{$+3.8$} & \grntxt{$+6.0$} & \grntxt{$+4.8$} \\
\midrule
RandAugment & \xmark & \xmark & $76.3 \pm 0.5$ & $75.5 \pm 0.2$ & $74.7 \pm 0.4$ \\
RandAugment + \schemename & \xmark & \xmark & $78.0 \pm 0.1$ & $77.8 \pm 0.1$ & $78.0 \pm 0.6$ \\
& & & \grntxt{$+1.7$} & \grntxt{$+2.3$} & \grntxt{$+3.3$} \\
\midrule
Basic & \cmark & \cmark & $79.8 \pm 0.3$ & $78.6 \pm 0.4$ & $78.1 \pm 1.6$ \\
Basic + \schemename & \cmark & \cmark & $79.8 \pm 0.3$ & $81.6 \pm 0.5$ & $81.0 \pm 0.4$ \\
& & & \gtxt{$\pm 0.0$} & \grntxt{$+3.0$} & \grntxt{$+2.9$} \\
\midrule
3-Augment & \xmark & \cmark & $79.1 \pm 0.1$ & $77.6 \pm 0.2$ & $75.3 \pm 0.4$ \\
3-Augment + \schemename & \xmark & \cmark & $81.4 \pm 0.1$ & $81.1 \pm 0.4$ & $79.8 \pm 0.1$ \\
& & & \grntxt{$+2.3$} & \grntxt{$+3.5$} & \grntxt{$+4.5$} \\
\midrule
RandAugment & \cmark & \cmark & $80.1 \pm 0.1$ & $81.9 \pm 0.3$ & $79.3 \pm 2.3$ \\
RandAugment + \schemename & \cmark & \cmark & $80.0 \pm 0.3$ & $81.9 \pm 0.2$ & $82.4 \pm 0.1$ \\
& & & \gtxt{$-0.1$} & \gtxt{$\pm 0.0$} & \grntxt{$+3.1$} \\
\bottomrule
\end{tabular}
}
\end{minipage}
\hfill
\begin{minipage}[t]{.37\textwidth}
\captionof{table}{ImageNet results of models trained on ImageNet with and without \schemename. \schemename improves the performance of most models, with a larger gain for larger models.}
\label{tab:imagenet-results}
\resizebox{\textwidth}{!}{\begin{tabular}{lccc}
\toprule
\multirow{2.5}{*}{Model} & \multicolumn{2}{c}{\makecell{Accuracy [\%]}} & \multirow{2.5}{*}{Delta} \\
\cmidrule(lr){2-3}
& w/o \schemename & w/ \schemename & \\
\midrule
ViT-S & $79.1\pm0.1$ & $81.4\pm0.1$ & \grntxt{$+2.3$} \\
ViT-B & $77.6\pm0.2$ & $81.1\pm0.4$ & \grntxt{$+3.5$} \\
ViT-L & $75.3\pm0.4$ & $79.8\pm0.1$ & \grntxt{$+4.5$} \\
\midrule
DeiT-S & $80.1 \pm 0.1$ & $80.0\pm0.3$ & \gtxt{$-0.1$} \\
DeiT-B & $81.9 \pm 0.3$ & $81.9\pm0.2$ & \gtxt{$\pm0.0$} \\
DeiT-L & $79.3\pm2.3$ & $82.4\pm0.1$ & \grntxt{$+3.1$} \\
\midrule
Swin-Ti & $77.9\pm0.2$ & $79.7\pm0.1$ & \grntxt{$+1.8$} \\
Swin-S & $79.4\pm0.1$ & $80.6\pm0.1$ & \grntxt{$+1.2$} \\
\midrule
ResNet-50 & $78.3\pm0.1$ & $78.8\pm0.1$ & \grntxt{$+0.5$} \\
ResNet-101 & $79.4\pm0.1$ & $80.4\pm0.1$ & \grntxt{$+1.0$} \\
\bottomrule
\end{tabular}}
\end{minipage}
\end{figure}
% \begin{table}[t]
% \caption{ImageNet results of models trained on ImageNet with and without \schemename. \schemename improves the performance of most models, with a larger gain for larger models.}
% \label{tab:imagenet-results}
% \centering
% \begin{subfigure}{.41\textwidth}
% \resizebox{\textwidth}{!}{\begin{tabular}{lccc}
% \toprule
% \multirow{2.5}{*}{Model} & \multicolumn{2}{c}{\makecell{ImageNet Accuracy [\%]}} & \multirow{2.5}{*}{Delta} \\
% \cmidrule(lr){2-3}
% & w/o \schemename & w/ \schemename & \\
% \midrule
% ViT-S & $79.1\pm0.1$ & $81.4\pm0.1$ & \grntxt{$+2.3$} \\
% ViT-B & $77.6\pm0.2$ & $81.1\pm0.4$ & \grntxt{$+3.5$} \\
% ViT-L & $75.3\pm0.4$ & $79.8\pm0.1$ & \grntxt{$+4.5$} \\
% \midrule
% Swin-Ti & $77.9\pm0.2$ & $79.7\pm0.1$ & \grntxt{$+1.8$} \\
% Swin-S & $79.4\pm0.1$ & $80.6\pm0.1$ & \grntxt{$+1.2$} \\
% \bottomrule
% \end{tabular}}
% \end{subfigure}
% \hspace{5pt}
% \begin{subfigure}{.448\textwidth}
% \resizebox{\textwidth}{!}{\begin{tabular}{lccc}
% \toprule
% \multirow{2.5}{*}{Model} & \multicolumn{2}{c}{\makecell{ImageNet Accuracy [\%]}} & \multirow{2.5}{*}{Delta} \\
% \cmidrule(lr){2-3}
% & w/o \schemename & w/ \schemename & \\
% \midrule
% DeiT-S & $80.1 \pm 0.1$ & $80.0\pm0.3$ & \gtxt{$-0.1$} \\
% DeiT-B & $81.9 \pm 0.3$ & $81.9\pm0.2$ & \gtxt{$\pm0.0$} \\
% DeiT-L & $79.3\pm2.3$ & $82.4\pm0.1$ & \grntxt{$+3.1$} \\
% \midrule
% ResNet-50 & $78.3\pm0.1$ & $78.8\pm0.1$ & \grntxt{$+0.5$} \\
% ResNet-101 & $79.4\pm0.1$ & $80.4\pm0.1$ & \grntxt{$+1.0$} \\
% \bottomrule
% \end{tabular}}
% \end{subfigure}
% \end{table}
\section{Experiments}
\label{sec:experiments}
We conduct a comprehensive suit of experiments to validate the effectiveness of our approach,
comparing ImageNet training with and without \schemename for 10 different models and 5 data augmentation pipelines.
Furthermore, we assess the impact of using \schemename for pretraining on multiple fine-grained downstream datasets.
Finally, we exploit \schemename's control over the image distribution to quantify model behaviors and biases.
We always report the mean and standard deviation of three independent training runs.
\subsection{Image Classification Results}
\textbf{ImageNet training.}
\Cref{tab:imagenet-pipelines} analyzes the effect of \schemename under different data augmentation pipelines:
A \emph{basic} pipeline with RandomResizedCrop, Flip and ColorJitter, the \emph{3-Augment} pipeline from \cite{Touvron2022,Nauen2025} that also includes Grayscale, Solarization and GaussianBlur, as well as the widely used \emph{RandAugment}~\cite{Cubuk2020} based pipeline from DeiT~\cite{Touvron2021b}.
Additionally, we include MixUp~\cite{Zhang2018a} and CutMix~\cite{Yun2019} augmentations.
% We also include Mixup and CutMix.
We find that the effectiveness of \schemename depends on the interplay between model capacity and baseline augmentation strength.
When the baseline augmentation is weak or moderate, \schemename consistently improves ImageNet accuracy, with gains increasing for larger ViT models (up to $+6.0$ p.p.\ for ViT-B).
As the augmentation pipeline becomes stronger (e.g., RandAugment with MixUp and CutMix), ImageNet improvements diminish for smaller models, indicating that the baseline augmentation already saturates their capacity.
Importantly, even in cases where ImageNet accuracy does not improve, we consistently observe gains during downstream fine-tuning (see \Cref{tab:downstream-results}), suggesting that \schemename enhances representation quality beyond what is reflected by ImageNet accuracy.
\Cref{tab:imagenet-results} additionally compares performance of different model architectures.
ViT~\cite{Dosovitskiy2021}, Swin~\cite{Liu2021} and ResNet~\cite{He2016} (representing CNNs) are trained using the ``3-augment'' strategy, while DeiT~\cite{Touvron2021b} is trained using the ``RandAugment'' strategy.
Notably, \schemename improves performance across all tested architectures, including the ResNet models, % (up to $1$ p.p.),
demonstrating benefits beyond Transformers.
% We find that \schemename's improvements counteract the drop in performance for increasing model sizes.
% Without \schemename this drop is $3.8$ p.p. (ViT-S to L), while with \schemename it is reduced to $1.6$ p.p.
% For DeiT there is a drop of $0.8$ p.p. from small to large while when using \schemename there is a \emph{gain} of $2.4$ p.p.
\begin{table}[t]
\caption{Downstream accuracy in percent when finetuning on other datasets. Models are pretrained on ImageNet with and without \schemename. Pretraining using \schemename increases transformer downstream accuracy.
% on all datasets.
}
\label{tab:downstream-results}
\begin{subfigure}{.48\columnwidth}
\resizebox{\textwidth}{!}{\begin{tabular}{lcccccc}
\toprule
Model & \schemename & Aircraft & Cars & Flowers & Food & Pets \\
\midrule
ViT-S & \xmark & $72.4\pm1.0$ & $89.8\pm0.3$ & $94.5\pm0.2$ & $89.1\pm0.1$ & $93.8\pm0.2$ \\
ViT-S & \cmark & $78.6\pm0.5$ & $92.2\pm0.2$ & $95.5\pm0.2$ & $89.6\pm0.1$ & $94.5\pm0.2$ \\
& & \grntxt{$+6.2$} & \grntxt{$+2.4$} & \grntxt{$+1.0$} & \grntxt{$+0.5$} & \grntxt{$+0.7$} \\
\midrule
ViT-B & \xmark & $71.7\pm0.5$ & $90.0\pm0.2$ & $94.8\pm0.4$ & $89.8\pm0.2$ & $94.1\pm0.4$ \\
ViT-B & \cmark & $79.0\pm2.2$ & $93.3\pm0.1$ & $ 96.5\pm0.1$ & $90.9\pm0.1$ & $95.1\pm0.4$ \\
& & \grntxt{$+7.3$} & \grntxt{$+3.3$} & \grntxt{$+1.7$} & \grntxt{$+1.1$} & \grntxt{$+1.0$} \\
\midrule
ViT-L & \xmark & $72.1\pm1.0$ & $88.8\pm0.3$ & $94.4\pm0.3$ & $90.1\pm0.2$ & $94.2\pm0.4$ \\
ViT-L & \cmark & $77.6\pm1.2$ & $89.1\pm0.2$ & $96.6\pm0.1$ & $91.3\pm0.1$ & $95.1\pm0.1$ \\
& & \grntxt{$+5.5$} & \grntxt{$+0.3$} & \grntxt{$+2.2$} & \grntxt{$+1.2$} & \grntxt{$+0.9$} \\
\midrule
Swin-Ti & \xmark & $77.0\pm0.1$ & $91.3\pm0.6$ & $95.9\pm0.1$ & $90.0\pm0.2$ & $94.2\pm0.1$ \\
Swin-Ti & \cmark & $81.1\pm0.8$ & $92.8\pm0.4$ & $96.2\pm0.1$ & $90.4\pm0.3$ & $94.8\pm0.5$ \\
& & \grntxt{$+4.1$} & \grntxt{$+2.5$} & \grntxt{$+0.3$} & \grntxt{$+0.4$} & \grntxt{$+0.6$} \\
\midrule
Swin-S & \xmark & $75.7\pm1.4$ & $91.0\pm0.3$ & $95.9\pm0.5$ & $91.1\pm0.2$ & $94.4\pm0.1$ \\
Swin-S & \cmark & $81.4\pm0.2$ & $93.1\pm0.2$ & $96.3\pm0.3$ & $91.2\pm0.2$ & $94.9\pm0.3$ \\
& & \grntxt{$+5.7$} & \grntxt{$+2.1$} & \grntxt{$+1.4$} & \gtxt{$+0.1$} & \grntxt{$+0.5$} \\
\bottomrule
\end{tabular}}
\end{subfigure}
\hfill
\begin{subfigure}{.505\columnwidth}
\resizebox{\textwidth}{!}{\begin{tabular}{lcccccc}
\toprule
Model & \schemename & Aircraft & Cars & Flowers & Food & Pets \\
\midrule
DeiT-S & \xmark & $75.3\pm0.4$ & $91.1\pm0.2$ & $94.8\pm0.4$ & $89.2\pm0.2$ & $92.4\pm0.2$ \\
DeiT-S & \cmark & $76.8\pm0.8$ & $91.9\pm0.2$ & $95.2\pm0.3$ & $89.1\pm0.2$ & $92.3\pm0.4$ \\
& & \grntxt{$+1.5$} & \grntxt{$+0.8$} & \grntxt{$+0.4$} & \gtxt{$-0.1$} & \gtxt{$-0.1$} \\
\midrule
DeiT-B & \xmark & $77.0\pm1.2$ & $92.9\pm0.2$ & $96.1\pm0.2$ & $91.2\pm0.1$ & $93.3\pm0.4$ \\
DeiT-B & \cmark & $79.3\pm0.3$ & $93.1\pm0.1$ & $96.4\pm0.2$ & $91.3\pm0.1$ & $93.3\pm0.1$ \\
& & \grntxt{$+2.3$} & \gtxt{$+0.2$} & \grntxt{$+0.3$} & \gtxt{$+0.1$} & \gtxt{$\pm0.0$} \\
\midrule
DeiT-L & \xmark & $72.8\pm5.5$ & $92.8\pm1.0$ & $95.8\pm1.5$ & $90.5\pm2.6$ & $92.4\pm2.0$ \\
DeiT-L & \cmark & $78.8\pm0.8$ & $93.8\pm0.2$ & $97.0\pm0.2$ & $92.0\pm0.2$ & $93.5\pm0.2$ \\
& & \grntxt{$+6.0$} & \grntxt{$+1.0$} & \grntxt{$+1.2$} & \grntxt{$+1.5$} & \grntxt{$+1.1$} \\
\midrule
ResNet-50 & \xmark & $78.2\pm0.5$ & $89.8\pm0.2$ & $91.7\pm0.4$ & $84.4\pm0.2$ & $93.7\pm0.3$ \\
ResNet-50 & \cmark & $80.3\pm0.4$ & $90.4\pm0.2$ & $91.7\pm0.2$ & $84.5\pm0.2$ & $93.7\pm0.3$ \\
& & \grntxt{$+2.1$} & \grntxt{$+0.6$} & \gtxt{$\pm0.0$} & \gtxt{$+0.1$} & \gtxt{$\pm0.0$} \\
\midrule
ResNet-101 & \xmark & $78.4\pm0.6$ & $90.3\pm0.1$ & $91.2\pm0.5$ & $86.0\pm0.2$ & $94.3\pm0.2$ \\
ResNet-101 & \cmark & $81.4\pm0.5$ & $91.3\pm0.1$ & $92.9\pm0.2$ & $86.3\pm0.1$ & $94.0\pm0.3$ \\
& & \grntxt{$+3.0$} & \grntxt{$+1.3$} & \grntxt{$+1.7$} & \grntxt{$+0.3$} & \textcolor{red}{$-0.3$} \\
\bottomrule
\end{tabular}}
\end{subfigure}
\end{table}
\textbf{Downstream tasks.} To assess the transferability of \schemename-trained models, we finetune models pretrained on ImageNet with and without \schemename on five fine-grained datasets:
FGVC-Aircraft \cite{Maji2013}, Stanford Cars~\cite{Dehghan2017}, Oxford Flowers \cite{Nilsback2008}, Food-101 \cite{Kaur2017}, and Oxford-IIIT Pets \cite{Parkhi2012}.
% While for ResNets, the performance of both training datasets is about the same,
In \Cref{tab:downstream-results} we see transformer accuracies improve on all these datasets by up to 7.3 p.p.
% and a reduction of error rate of up to $39.3\%$.
% Notably, training with \name increases the downstream performance of DeiT-S and DeiT-B, even though the ImageNet results were the same.
% This demonstrates that the improved representations from training on \name translate to superior performance beyond gains from better ImageNet performance.
Notably, training with \schemename boosts the downstream performance of DeiT-S and DeiT-B, despite similar ImageNet accuracy.
This shows, that the improved representations from training with \schemename translate to gains beyond better ImageNet scores.
% not only on ImageNet, but also on fine-grained image classification tasks.
\begin{table}[t]
\caption{Evaluation of models trained on ImageNet with and without \schemename. \schemename generally increases models' robustness to different image distribution shifts. Note that ViT-S \emph{with} \schemename outperforms DeiT-S, the only model where \schemename does not increase robustness.}
\label{tab:robustness-datasets}
\begin{subfigure}{.485\textwidth}
\resizebox{\textwidth}{!}{
\begin{tabular}{lccccccc}
\toprule
Model & w/ \schemename & IN-Hard & IN-A & IN-C & IN-R & IN-V2 \\
\midrule
ViT-S & \xmark & $18.1 \pm 0.6$ & $18.8 \pm 0.2$ & $44.7 \pm 0.8$ & $41.6 \pm 0.6$ & $67.3 \pm 0.4$ \\
ViT-S & \cmark & $21.0 \pm 0.4$ & $26.5 \pm 0.4$ & $52.6 \pm 0.6$ & $49.8 \pm 0.3$ & $70.6 \pm 0.1$ \\
& & \grntxt{$+2.9$} & \grntxt{$+7.7$} & \grntxt{$+7.9$} & \grntxt{$+8.1$} & \grntxt{$+3.3$} \\
\midrule
ViT-B & \xmark & $17.0 \pm 0.4$ & $15.8 \pm 0.7$ & $40.4 \pm 0.8$ & $38.4 \pm 0.7$ & $65.1 \pm 0.6$ \\
ViT-B & \cmark & $22.0 \pm 0.9$ & $31.9 \pm 1.5$ & $51.6 \pm 1.8$ & $48.7 \pm 1.7$ & $70.3 \pm 0.9$ \\
& & \grntxt{$+5.0$} & \grntxt{$+16.0$} & \grntxt{$+11.2$} & \grntxt{$+10.3$} & \grntxt{$+5.2$} \\
\midrule
ViT-L & \xmark & $15.6 \pm 0.4$ & $11.3 \pm 0.9$ & $38.4 \pm 1.0$ & $36.8 \pm 0.8$ & $61.6 \pm 0.8$ \\
ViT-L & \cmark & $20.6 \pm 0.1$ & $30.4 \pm 0.5$ & $48.2 \pm 0.7$ & $46.0 \pm 0.4$ & $68.7 \pm 0.3$ \\
& & \grntxt{$+5.0$} & \grntxt{$+19.0$} & \grntxt{$+9.8$} & \grntxt{$+9.3$} & \grntxt{$+7.1$} \\
\midrule
Swin-Ti & \xmark & $16.2 \pm 0.4$ & $15.0 \pm 0.3$ & $36.0 \pm 0.8$ & $36.6 \pm 0.2$ & $65.5 \pm 0.4$ \\
Swin-Ti & \cmark & $18.3 \pm 0.3$ & $20.3 \pm 0.4$ & $41.4 \pm 0.8$ & $41.4 \pm 0.2$ & $68.2 \pm 0.4$ \\
& & \grntxt{$+2.2$} & \grntxt{$+5.4$} & \grntxt{$+5.4$} & \grntxt{$+4.8$} & \grntxt{$+2.7$} \\
\midrule
Swin-S & \xmark & $18.2 \pm 0.3$ & $19.4 \pm 0.3$ & $39.0 \pm 0.7$ & $39.1 \pm 0.2$ & $67.5 \pm 0.1$ \\
Swin-S & \cmark & $20.5 \pm 0.1$ & $27.7 \pm 0.4$ & $45.6 \pm 0.8$ & $44.1 \pm 0.3$ & $69.6 \pm 0.1$ \\
& & \grntxt{$+2.2$} & \grntxt{$+8.4$} & \grntxt{$+6.6$} & \grntxt{$+5.0$} & \grntxt{$+2.2$} \\
\bottomrule
\end{tabular}
}
\end{subfigure}
\hfill
\begin{subfigure}{.505\textwidth}
\resizebox{\textwidth}{!}{
\begin{tabular}{lccccccc}
\toprule
Model & w/ \schemename & IN-Hard & IN-A & IN-C & IN-R & IN-V2 \\
\midrule
DeiT-S & \xmark & $19.5 \pm 0.2$ & $18.4 \pm 0.3$ & $58.8 \pm 0.7$ & $43.0 \pm 0.1$ & $68.8 \pm 0.2$ \\
DeiT-S & \cmark & $18.5 \pm 0.5$ & $17.3 \pm 1.0$ & $57.0 \pm 0.9$ & $43.8 \pm 0.2$ & $68.7 \pm 0.6$ \\
& & \rdtxt{$-1.0$} & \rdtxt{$-1.1$} & \rdtxt{$-1.8$} & \grntxt{$+0.8$} & \gtxt{$-0.1$} \\
\midrule
DeiT-B & \xmark & $22.6 \pm 0.2$ & $26.0 \pm 0.2$ & $62.1 \pm 1.0$ & $45.6 \pm 1.9$ & $70.6 \pm 0.9$ \\
DeiT-B & \cmark & $22.6 \pm 0.2$ & $25.0 \pm 0.3$ & $62.8 \pm 0.6$ & $47.7 \pm 0.8$ & $70.8 \pm 0.5$ \\
& & \gtxt{$\pm 0.0$} & \rdtxt{$-1.0$} & \grntxt{$+0.8$} & \grntxt{$+2.0$} & \gtxt{$+0.2$} \\
\midrule
DeiT-L & \xmark & $21.2 \pm 2.0$ & $20.2 \pm 3.4$ & $59.3 \pm 4.3$ & $41.3 \pm 2.7$ & $66.9 \pm 2.8$ \\
DeiT-L & \cmark & $23.4 \pm 0.3$ & $28.8 \pm 2.0$ & $63.4 \pm 0.7$ & $47.8 \pm 0.6$ & $71.6 \pm 0.5$ \\
& & \grntxt{$+2.2$} & \grntxt{$+8.7$} & \grntxt{$+4.1$} & \grntxt{$+6.5$} & \grntxt{$+4.7$} \\
\midrule
ResNet50 & \xmark & $16.1 \pm 0.2$ & $9.7 \pm 0.1$ & $38.0 \pm 1.0$ & $40.5 \pm 0.6$ & $66.8 \pm 0.4$ \\
ResNet50 & \cmark & $17.2 \pm 0.1$ & $10.8 \pm 0.4$ & $41.0 \pm 0.7$ & $43.7 \pm 0.3$ & $67.5 \pm 0.1$ \\
& & \grntxt{$+1.1$} & \grntxt{$+1.1$} & \grntxt{$+3.0$} & \grntxt{$+3.2$} & \grntxt{$+0.7$} \\
\midrule
ResNet101 & \xmark & $18.2 \pm 0.4$ & $14.3 \pm 0.1$ & $41.7 \pm 0.7$ & $42.3 \pm 0.1$ & $67.7 \pm 0.5$ \\
ResNet101 & \cmark & $19.9 \pm 0.2$ & $17.6 \pm 0.5$ & $46.3 \pm 0.6$ & $46.3 \pm 0.3$ & $69.5 \pm 0.3$ \\
& & \grntxt{$+1.7$} & \grntxt{$+3.2$} & \grntxt{$+4.6$} & \grntxt{$+4.0$} & \grntxt{$+1.8$} \\
\bottomrule
\end{tabular}
}
\end{subfigure}
\end{table}
\subsection{Bias and Robustness Evaluation}
Beyond its use for training, \schemename's unique properties and controlled data generation capabilities make it a powerful tool for analyzing behavior and biases of black-box models.
We exploit this in two complementary ways.
First, we ask whether \schemename-trained models are more robust on \emph{external} ImageNet robustness benchmarks that are not generated by our pipeline.
Second, we use \schemename's fine-grained control for targeted evaluation of specific dimensions of model bias, such as background reliance and center/size bias.
% Together, these experiments allow us to both \emph{probe} and \emph{improve} robustness along clearly defined axes.
% This combination of standard benchmarks and controlled probes allows us to both quantify robustness improvements and attribute them to changes in particular model behaviors.
\textbf{Robustness on External Distribution Shifts.}
\Cref{tab:robustness-datasets} summarizes accuracy on five widely used ImageNet robustness benchmarks: ImageNet-Hard~\cite{Taesiri2023}, ImageNet-A~\cite{Hendrycks2021}, ImageNet-C~\cite{Hendrycks2019}, ImageNet-R~\cite{Hendrycks2021a}, and ImageNetV2~\cite{Recht2019}.
Across ViTs, Swin Transformers, and ResNets, incorporating \schemename during training generally improves robustness to all considered distribution shifts.
For ViTs, the gains are substantial: for example, ViT-B improves from $15.8\%$ to $31.9\%$ accuracy on ImageNet-A ($+16.0$ p.p.) and from $40.4\%$ to $51.6\%$ on ImageNet-C ($+11.2$ p.p.), with similar improvements for ViT-S and ViT-L.
Swin also benefits consistently, with increases of roughly $2$--$8$ p.p. on most benchmarks, and ResNet sees smaller but steady gains (e.g., up to $+4.6$ points on ImageNet-C).
For DeiT, the picture is more nuanced: DeiT-B and DeiT-L still enjoy robustness improvements, whereas DeiT-S exhibits small decreases on several benchmarks.
Interestingly, however, ViT-S trained with \schemename outperforms the DeiT-S baseline.
This suggests that controlled composition can partially close the robustness gap between lightly and heavily regularized models.
Overall, the consistent improvements on corruption-based, natural and hard examples indicate that the compositional invariances induced by \schemename extend beyond the specific foreground/background manipulations used in its construction.
\begin{figure*}[t]
\centering
\includegraphics[width=\textwidth]{img/bg_robustness.pdf}
\caption{Evaluation of background robustness on ImageNet + \schemename, ImageNet9~\cite{Xiao2020} and CounterAnimal~\cite{Wang2024f}.
We plot the in-distribution (top of arrow) and the out-of-distribution (bottom of arrow) accuracy when training with and without \schemename.
We annotate each arrow with its length $\Delta$.
Training with \schemename improves the background robustness of all transformers by mostly boosting the out-of-distribution accuracy.
}
\label{fig:background-robustness}
\end{figure*}
\textbf{Background Robustness.}
% By adjusting the background distribution from using a background from an image of the same class as the foreground to using any background, we can evaluate the robustness of models to shifts in the background distribution.
% We assess background robustness by changing the background distribution, comparing accuracy with backgrounds of the same class as the foreground to using any background.
We assess the robustness of models to shifts in the background distribution from a class-related background to any background.
% We define the background robustness coefficient to be the accuracy of a model on \name when using the same class background divided by the accuracy when using any background:
% Background robustness is defined to be the ratio of accuracy on \name with same-class backgrounds to accuracy with any background:
% \begin{align}
% \text{Background Robustness} = \frac{\text{Acc}(\name_\text{all})}{\text{Acc}(\name_\text{same})}
% \end{align}
% It represents the relative drop in performance under a background distribution shift.
\Cref{fig:background-robustness} presents the background robustness results for three datasets: ImageNet with \schemename (all backgrounds vs. backgrounds of same class), ImageNet9~\cite{Xiao2020} (random backgrounds vs. original backgrounds), and CounterAnimal~\cite{Wang2024f} (counter vs. common background).
The top triangle of each arrow represents the in-distribution backgrounds and the bottom triangle represents the out-of-distribution ones.
We follow ImageNet9 and CounterAnimal and assess the background robustness in terms of the accuracy gap when evaluating a model on images of normal background distribution compared to out-of-distribution backgrounds (length of each arrow; $\Delta$).
% When trained on ImageNet, smaller models generally exhibit greater robustness to changes in the background distribution than larger models and ResNet is more robust than the tested Transformer models.
Crucially, \schemename improves the background robustness of all models and across datasets, reducing the background-gap by boosting the performance on the out-of-background-distribution samples more than the in-distribution ones.
We find a similar trend for the Corner-Cases~\cite{Fatima2025} dataset (see supplementary), highlighting the generalization benefits of \schemename to unusual image compositions.
\begin{figure*}[t]
\centering
\includegraphics[width=\textwidth]{img/fg_focus.pdf}
\caption{Evaluation of the foreground focus (\Cref{eq:fg-focus}) using GradCam, GradCam++ and IntegratedGradients (IG) of models trained on ImageNet. Training with \schemename improves the foreground focus of almost all models.}
\label{fig:foreground-focus}
\end{figure*}
\textbf{Foreground Focus.}
Leveraging our inherent knowledge of the foreground masks when using \schemename, as well as common XAI techniques~\cite{Selvaraju2016,Chattopadhay2018,Sundararajan2017}, we can evaluate a model's focus on the foreground object.
% I.e. we measure how much the model's decision depends on the foreground.
We can directly evaluate ImageNet-trained models, but this technique can also be extended to other datasets without relying on manually annotated foreground masks.
To evaluate the foreground focus, we employ Grad-CAM \cite{Selvaraju2016}, Grad-CAM++ \cite{Chattopadhay2018} and IntegratedGradients (IG) \cite{Sundararajan2017} to compute the per-pixel importance of an image for the model's prediction.
The foreground focus is defined to be the ratio of the foreground's relative importance to its relative size in the image:
\begin{align} \label{eq:fg-focus}
\text{FG Focus}(\text{img}) = \frac{\text{Area}(\text{img}) \hspace{3pt} \text{Importance}(\text{fg})}{\text{Area}(\text{fg}) \hspace{3pt} \text{Importance}(\text{img})}
\end{align}
If all pixels uniformly receive the same importance value, the foreground focus is one.
The foreground focus of a model is its average focus over all test images.
\Cref{fig:foreground-focus} presents our findings.
Using \schemename significantly increases the foreground focus of ViT, DeiT and ResNet across all XAI metrics.
% I.e. \schemename-trained models base their decision more on the foreground object compared to the background than models trained without \schemename.
% For Swin, the foreground focus stagnates when measured using GradCam and GradCam++, but almost doubles when using IG.
% We hypothesize that Swin's below-uniform foreground focus reported with GradCam is due to its specific implementation for Swin.
We hypothesize Swin's below-uniform foreground focus with GradCam is due to its specific implementation.
% These differences might be due to the way GradCam is calculated for Swin \todo{cite package website where this is from} and the \todo{common critique of GradCam}.
\begin{table}[t]
\caption{
% Evaluation of the center bias.
Accuracy relative to the center accuracy of multiple instantiations of the models when the foreground objects is in different cells of a $3 \times 3$ grid.
We calculate center bias according to \Cref{eq:center-bias}.
Using \schemename significantly reduces models' center bias.}
\label{tab:center-bias}
\begin{subfigure}{.48\columnwidth}
\resizebox{\textwidth}{!}{
\begin{tabular}{lccc}
\toprule
\multirow{2.5}{*}{Model} & \multicolumn{2}{c}{\makecell{Center Bias [\%] when trained}} & \multirow{2.5}{*}{Delta} \\
\cmidrule(lr){2-3}
& w/o \schemename & w/ \schemename \\
\midrule
ViT-S & \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-S_ImageNet_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-S_ImageNet_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-S_ImageNet_v3.pdf} & \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-S_RecombNet_all_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-S_RecombNet_all_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-S_RecombNet_all_v3.pdf} \\
& $25.5\pm0.8$ & $22.0\pm0.3$ & \grntxt{$-3.5$} \\
ViT-B & {\includegraphics[width=.08\columnwidth, valign=c]{img/ViT-B_ImageNet_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-B_ImageNet_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-B_ImageNet_v3.pdf}} & \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-B_RecombNet_all_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-B_RecombNet_all_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-B_RecombNet_all_v3.pdf} \\
& $25.4\pm0.4$ & $19.0\pm0.2$ & \grntxt{$-6.4$} \\
ViT-L & \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-L_ImageNet_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-L_ImageNet_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-L_ImageNet_v3.pdf} & \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-L_RecombNet_all_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-L_RecombNet_all_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ViT-L_RecombNet_all_v3.pdf} \\
& $24.3\pm1.1$ & $11.7\pm0.7$ & \grntxt{$-12.6$} \\
\midrule
Swin-Ti & {\includegraphics[width=.08\columnwidth, valign=c]{img/Swin-Ti_ImageNet_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/Swin-Ti_ImageNet_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/Swin-Ti_ImageNet_v3.pdf}} & {\includegraphics[width=.08\columnwidth, valign=c]{img/Swin-Ti_RecombNet_all_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/Swin-Ti_RecombNet_all_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/Swin-Ti_RecombNet_all_v3.pdf}} \\
& $25.0\pm0.7$ & $16.5\pm0.2$ & \grntxt{$-8.5$} \\
Swin-S & {\includegraphics[width=.08\columnwidth, valign=c]{img/Swin-S_ImageNet_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/Swin-S_ImageNet_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/Swin-S_ImageNet_v3.pdf}} & {\includegraphics[width=.08\columnwidth, valign=c]{img/Swin-S_RecombNet_all_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/Swin-S_RecombNet_all_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/Swin-S_RecombNet_all_v3.pdf}} \\
& $23.2\pm0.1$ & $15.6\pm0.2$ & \grntxt{$-7.6$} \\
\bottomrule
\end{tabular} }
\end{subfigure}
\hfill
\begin{subfigure}{.497\columnwidth}
\resizebox{\textwidth}{!}{
\begin{tabular}{lccc}
\toprule
\multirow{2.5}{*}{Model} & \multicolumn{2}{c}{\makecell{Center Bias [\%] when trained}} & \multirow{2.5}{*}{Delta} \\
\cmidrule(lr){2-3}
& w/o \schemename & w/ \schemename \\
\midrule
DeiT-S & {\includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-S_ImageNet_vNone.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-S_ImageNet_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-S_ImageNet_v3.pdf} } & {\includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-S_fornet_all_linear_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-S_fornet_all_linear_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-S_fornet_all_linear_v3.pdf}} \\
& $20.4 \pm 0.2$ & $21.2 \pm 0.1$ & \gtxt{$+0.8$} \\
DeiT-B & {\includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-B_ImageNet_vNone.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-B_ImageNet_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-B_ImageNet_v3.pdf} } & {\includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-B_fornet_all_cos_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-B_fornet_all_cos_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-B_fornet_all_cos_v3.pdf}} \\
& $19.0 \pm 0.7$ & $19.0 \pm 0.2$ & \gtxt{$\pm0.0$} \\
DeiT-L & { \includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-L_ImageNet_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-L_ImageNet_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-L_ImageNet_v3.pdf} } & { \includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-L_fornet_all_cos_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-L_fornet_all_cos_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/DeiT-L_fornet_all_cos_v3.pdf} } \\
& $21.2 \pm 0.2$ & $18.0 \pm 0.2$ & \grntxt{$-3.2$} \\
\midrule
ResNet50 & {\includegraphics[width=.08\columnwidth, valign=c]{img/ResNet50_ImageNet_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ResNet50_ImageNet_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ResNet50_ImageNet_v3.pdf}} & {\includegraphics[width=.08\columnwidth, valign=c]{img/ResNet50_RecombNet_all_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ResNet50_RecombNet_all_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ResNet50_RecombNet_all_v3.pdf}} \\
& $26.3\pm0.3$ & $19.7\pm0.3$ & \grntxt{$-6.6$} \\
ResNet101 & {\includegraphics[width=.08\columnwidth, valign=c]{img/ResNet101_ImageNet_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ResNet101_ImageNet_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ResNet101_ImageNet_v3.pdf}} & {\includegraphics[width=.08\columnwidth, valign=c]{img/ResNet101_RecombNet_all_v1.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ResNet101_RecombNet_all_v2.pdf} \includegraphics[width=.08\columnwidth, valign=c]{img/ResNet101_RecombNet_all_v3.pdf}} \\
& $23.0\pm0.3$ & $19.9\pm0.2$ & \grntxt{$-3.1$} \\
\bottomrule
\end{tabular} }
\end{subfigure}
\centering
\includegraphics[width=.5\columnwidth]{img/colorbar_horizontal.pdf}
\end{table}
\textbf{Center Bias.}
With \schemename we have unique control over the position of the foreground object in the image.
This lets us quantify the center bias of models trained with and without \schemename.
We divide the image into a $3 \times 3$ grid and evaluate model accuracy when the (scaled-down) foreground object is in each of the $9$ grid cells.
Each cell's accuracy is divided by the accuracy in the center cell for normalization, which gives us the relative performance drop when the foreground is in each part of the image.
The center bias is calculated as one minus the average of the minimum performance of a corner cell and the minimum performance of a side cell:
% \begin{align}
% \begin{split}
% & \text{Center Bias} = \\
% & \hspace{7pt} 1 - \frac{\min\limits_{a, b \in \{0, 2\}} \text{Acc}(\text{cell}_{(a, b)}) + \min\limits_{\substack{a=1 \text{ or } b=1 \\ a \neq b}} \text{Acc}(\text{cell}_{(a, b)})}{2 \text{Acc}(\text{cell}_{(1, 1)})}
% \end{split}
% \end{align}
\begin{align} \label{eq:center-bias}
\text{Center Bias} = 1 - \frac{\min\limits_{c \in \text{sides}} \text{Acc}(c) + \min\limits_{c \in \text{corners}} \text{Acc}(c)}{2 \text{Acc}(c_\text{center})}
\end{align}
\Cref{tab:center-bias} visualizes the center bias of three instantiations of each model.
Performance is generally highest in the center and lowest in the four corners.
Interestingly, ImageNet-trained models perform slightly better when the foreground object is on the right side of the image, compared to the left side, despite our use of random flipping with a probability of $0.5$ during training.
% Training on \name reduces the center bias of all models by at least half.
Using \schemename significantly reduces center bias across models, with a more uniform performance especially across the middle row.
% On corner-cases (see supplementary) we find that
% Their accuracy is higher in the center left and right cells than in the center top and bottom ones, which is not the case for ImageNet-trained models.
% This demonstrates that \schemename promotes a more uniform spatial attention distribution, counteracting the center-bias of ImageNet.
Thus, \schemename makes the model recognize objects across a wider spatial distribution, counteracting the center-bias of ImageNet.
\begin{figure}[t!]
\centering
\includegraphics[width=\columnwidth]{img/size_bias_wide.pdf}
\caption{Evaluation of the size bias of models trained on ImageNet. We plot the accuracy relative to the accuracy when using the default size ($f_\text{size} = 1.0$).}
\label{fig:size-bias}
\end{figure}
\textbf{Size Bias.}
Finally, we evaluate the impact of different sized foreground objects on the accuracy.
For this evaluation, we use the \emph{mean} foreground size strategy.
We introduce a size factor $f_\text{size}$ by which we additionally scale the foreground object before pasting it onto the background.
Results are normalized by the accuracy when using $f_\text{size} = 1.0$.
\Cref{fig:size-bias} shows the size bias curves of models trained with and without \schemename.
% When training on \name, the resulting model keeps it's good performance on smaller foreground objects, while models trained on ImageNet fall of faster and lower.
Models trained using \schemename perform better, especially with smaller foreground objects.
%, when ImageNet-trained models exhibit a more rapid performance decline.
Therefore, \schemename-training improves robustness to variations in object scale, especially for larger models.
\subsection{Design Choices of \schemename}
We next analyze key components of \schemename, focusing on three questions: how it compares to simple copy-paste, how background choice affects performance, and how reliably labels are preserved after recomposition.
Additional ablations over variants and hyperparameters are provided in the supplementary material.
\begin{table}[t]
\caption{Comparison of \schemename and simple Copy-Paste methods. We train ViT-S on ImageNet using the same 3-augment data augmentation on top of the copy-paste augmentation.}
\label{tab:copy-paste-comparison}
\centering
\resizebox{.66\columnwidth}{!}{
\begin{tabular}{lcc S[table-format=+2.1,retain-explicit-plus,detect-inline-weight=math,detect-weight=true]}
\toprule
Augmentation & labels & \makecell{ Accuracy [\%]} & {\makecell{Delta \\to Prev.}} \\
\midrule
% Baseline & & $79.1 \pm 0.1$ \\
3-Augment + \textbf{Simple Copy-Paste} & bg & $31.3 \pm 0.6$ & \\
+ mixed labels & fg + bg & $32.0 \pm 0.8$ & +0.7 \\
+ fg labels & fg & $31.6 \pm 0.9$ & -0.4 \\
+ \emph{range} foreground size variation & \gtxt{fg} & $43.0 \pm 1.2$ & \bfseries +11.4 \\
+ infilled backgrounds & \gtxt{fg} & $68.7 \pm 0.2$ & \bfseries +25.7 \\
+ \emph{cos} mixing strategy & \gtxt{fg} & $81.2 \pm 0.1$ & \bfseries +12.5 \\
+ edge smoothing & \gtxt{fg} & $81.3 \pm 0.1$ & +0.1 \\
+ background pruning$=$ \textbf{\schemename} & \gtxt{fg} & $81.4 \pm 0.1$ & +0.1 \\
\bottomrule
\end{tabular}}
\end{table}
\textbf{Comparison to Simple Copy-Paste.}
We compare \schemename to a simple adaption of the Copy-Paste augmentation inspired by \cite{Ge2023,Ghiasi2021,Shermaine2025} in \Cref{tab:copy-paste-comparison}.
Contrary to semantic segmentation we do not have foreground masks available.
Thus, we paste the extracted objects from \textbf{\schemename's segmentation stage} onto normal ImageNet images.
% Since such images do not have straight forward classification labels, we test multiple possibilities.
We observe 3 large jumps in accuracy: (\textbf{1}) From our \emph{range} foreground size variation (+11.4\%), (\textbf{2}) from using our infilled backgrounds instead of images from the dataset (+25.7\%), and (\textbf{3}) from our \emph{cos} mixing strategy with non-augmented images (+12.5\%).
\schemename's changes to the naive copy-paste augmentation are thus imperative for good classification performance.
\begin{figure}[t]
\begin{minipage}[c]{.49\textwidth}
\centering
\includegraphics[width=\textwidth]{img/strategy.pdf}
\captionof{figure}{We compare Original, Same-class, and All-classes background selection using ViT-Ti and ViT-S backbones on TinyImageNet.
Increasing background diversity consistently improves classification accuracy.
}
\label{fig:background-strategy}
\end{minipage}
\hfill
\begin{minipage}[c]{.49\textwidth}
\centering
\includegraphics[width=\textwidth]{img/mask_expansion.pdf}
\captionof{figure}{
We vary the foreground mask area for TinyImageNet by shrinking or expanding masks relative to the original outline and report accuracy when training on $100\%$ augmented samples.
Performance is stable for expanded masks and degrades rapidly after shrinking masks.
}
\label{fig:mask-expansion}
\end{minipage}
\end{figure}
\textbf{Background Choice Strategy.}
\Cref{fig:background-strategy} shows the effect of background selection on TinyImageNet accuracy, where we trade off diversity against context plausibility.
% Using the original inpainted background yields the lowest accuracy, indicating limited regularization from contextual cues.
% Sampling backgrounds from the same class provides a modest but consistent improvement, suggesting that mild context variation encourages robustness while preserving semantic plausibility.
The best performance is achieved by sampling backgrounds from all classes, which introduces substantial context shifts, but leads to the strongest accuracy gains for both ViT-Ti and ViT-S.
Thus, aggressive background diversification is more important than context plausibility and acts as an effective form of context-based regularization rather than introducing harmful noise.
\textbf{Label Integrity.}
% We assess the label integrity of \schemename, i.e., whether object labels remain correct after recombination, by verifying that the intended object is accurately extracted.
% To this end, we leverage the object bounding box annotations provided in the ImageNet validation set.
% Specifically, we compute the \emph{box precision}, defined as the fraction of the predicted mask area that lies within the ground-truth bounding box, obtaining a mean value of $91\%$.
% In addition, we measure the \emph{box-to-box IoU}, computed as the IoU between the tight bounding box enclosing the predicted mask and the tight bounding box of the ground-truth annotation, which yields a high $76.1\%$.
% Qualitative examples of the predicted masks and bounding boxes are provided in the supplementary material.
% We additionally test label integrity under systematic mask perturbations by expanding or shrinking the foreground masks before composition.
% Concretely, starting from the original outline, we erode or dilate the mask such that the foreground area changes by some percentage.
% \Cref{fig:mask-expansion} shows that accuracy is relatively stable for expanded masks, but drops off significantly for eroded masks, consistent with cropping away semantically important object parts.
% This experiment suggests, that \schemename is relatively robust to artifacts from including an object's original background in the foreground mask.
% Overall, these results indicate that the segmentation stage of \schemename reliably isolates the target class object, thereby preserving label correctness after recombination.
To quantify whether recombined images still depict the intended class, we evaluate the segmentation stage of \schemename on ImageNet validation boxes.
Our predicted masks achieve a mean box precision of $91.0\%$ (fraction of mask area inside the ground-truth bounding boxes of the ImageNet validation set) and a high box-to-box IoU of $76.1\%$, indicating that they tightly capture the target object.
Qualitative examples of the predicted masks and bounding boxes are provided in the supplementary material.
We further probe robustness to mask imprecision by eroding or dilating masks such that the foreground area changes by a fixed percentage before composition.
As shown in \Cref{fig:mask-expansion}, accuracy remains stable for expansions but drops sharply under erosion, consistent with removing semantically important object parts.
Together, these results suggest that (\textit{i}) \schemename reliably isolates the target objects and preserves label integrity and that (\textit{ii}) \schemename is robust to artifacts from an object's original background and degrades mainly when the foreground no longer contains the full object.