  Most previous approaches for analyzing food images have relied on extensively
annotated datasets, resulting in significant human labeling expenses due to the
varied and intricate nature of such images. Inspired by the effectiveness of
contrastive self-supervised methods in utilizing unlabelled data, we explore
leveraging these techniques on unlabelled food images. In contrastive
self-supervised methods, two views are randomly generated from an image by data
augmentations. However, regarding food images, the two views tend to contain
similar informative contents, causing large mutual information, which impedes
the efficacy of contrastive self-supervised learning. To address this problem,
we propose Feature Suppressed Contrast (FeaSC) to reduce mutual information
between views. As the similar contents of the two views are salient or highly
responsive in the feature map, the proposed FeaSC uses a response-aware scheme
to localize salient features in an unsupervised manner. By suppressing some
salient features in one view while leaving another contrast view unchanged, the
mutual information between the two views is reduced, thereby enhancing the
effectiveness of contrast learning for self-supervised food pre-training. As a
plug-and-play module, the proposed method consistently improves BYOL and
SimSiam by 1.70\% $\sim$ 6.69\% classification accuracy on four publicly
available food recognition datasets. Superior results have also been achieved
on downstream segmentation tasks, demonstrating the effectiveness of the
proposed method.
