Robust Contrastive Learning Using Negative Samples with Diminished Semantics Authors

Songwei Ge, Shlok Kumar Mishra, Haohan Wang, Chun-Liang Li, David Jacobs

Abstract:

Unsupervised learning has recently made exceptional progress because of the development of more effective contrastive learning methods. However, CNNs are prone
to depend on low-level features that humans deem non-semantic. This dependency
has been conjectured to induce a lack of robustness to image perturbations or
domain shift. In this paper, we show that by generating carefully designed negative
samples, contrastive learning can learn more robust representations with less dependence on such features. Contrastive learning utilizes positive pairs that preserve
semantic information while perturbing superficial features in the training images.
Similarly, we propose to generate negative samples in a reversed way, where only
the superfluous instead of the semantic features are preserved. We develop two
methods, texture-based and patch-based augmentations, to generate negative samples. These samples achieve better generalization, especially under out-of-domain
settings. We also analyze our method and the generated texture-based samples,
showing that texture features are indispensable in classifying particular ImageNet
classes and especially finer classes. We also show that model bias favors texture
and shape features differently under different test settings. Our code, trained
models, and ImageNet-Texture dataset can be found at https://github.com/
SongweiGe/Contrastive-Learning-with-Non-Semantic-Negatives.