19 lines
2.0 KiB
TeX
19 lines
2.0 KiB
TeX
% !TeX root = ../main.tex
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\begin{abstract}
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Transformers, particularly Vision Transformers (ViTs), have achieved state-of-the-art performance in large-scale image classification.
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However, they often require large amounts of data and can exhibit biases, such as center or size bias, that limit their robustness and generalizability.
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This paper introduces \schemename, a novel data augmentation operation that addresses these challenges by explicitly imposing invariances into the training data, which are otherwise part of the neural network architecture.
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% This paper introduces \name, a novel dataset derived from ImageNet that addresses these challenges.
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\schemename is constructed by using pretrained foundation models to separate and recombine foreground objects with different backgrounds.
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% enabling fine-grained control over image composition during training.
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% Missing sentence here of how you use it to generate data in what way and with what purpose wrt to bias
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This recombination step enables us to take fine-grained control over object position and size, as well as background selection.
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% It thus increases the data diversity and effective number of training samples.
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We demonstrate that using \schemename significantly improves the accuracy of ViTs and other architectures by up to 4.5 percentage points (p.p.) on ImageNet, which translates to 7.3 p.p. on downstream tasks.
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% Importantly, \schemename enables novel ways of analyzing model behavior and quantifying biases.
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Importantly, \schemename not only improves accuracy but also opens new ways to analyze model behavior and quantify biases.
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Namely, we introduce metrics for background robustness, foreground focus, center bias, and size bias and show that using \schemename during training substantially reduces these biases.
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In summary, \schemename provides a valuable tool for analyzing and mitigating biases, enabling the development of more robust and reliable computer vision models.
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Our code and dataset are publicly available at \code{<url>}.
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\end{abstract} |