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\section{Future Work}
\label{sec:future-work}

While \name demonstrates significant improvements in image classification performance and provides valuable tools for model analysis, several avenues for future research remain.
Investigating more sophisticated segmentation methods, could improve the quality of foreground/background separation and further enhance \name's effectiveness.
Developing strategies for adaptively selecting backgrounds and recombination parameters (size, position, etc.) based on the specific foreground object or model performance could lead to more efficient training.
This could involve reinforcement learning or other online learning techniques.
We also want to explore the potential of \name for other datasets and for unsupervised or self-supervised learning tasks.
Another potential fruitful direction is exploring the use of \name, and the insights gained from the bias analyses, to develop specific training techniques or architectural modifications that explicitly mitigate identified biases