  Deep learning has made significant advances in computer vision, particularly
in image classification tasks. Despite their high accuracy on training data,
deep learning models often face challenges related to complexity and
overfitting. One notable concern is that the model often relies heavily on a
limited subset of filters for making predictions. This dependency can result in
compromised generalization and an increased vulnerability to minor variations.
While regularization techniques like weight decay, dropout, and data
augmentation are commonly used to address this issue, they may not directly
tackle the reliance on specific filters. Our observations reveal that the heavy
reliance problem gets severe when slow-learning filters are deprived of
learning opportunities due to fast-learning filters. Drawing inspiration from
image augmentation research that combats over-reliance on specific image
regions by removing and replacing parts of images, our idea is to mitigate the
problem of over-reliance on strong filters by substituting highly activated
features. To this end, we present a novel method called Catch-up Mix, which
provides learning opportunities to a wide range of filters during training,
focusing on filters that may lag behind. By mixing activation maps with
relatively lower norms, Catch-up Mix promotes the development of more diverse
representations and reduces reliance on a small subset of filters. Experimental
results demonstrate the superiority of our method in various vision
classification datasets, providing enhanced robustness.
