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Article overview
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Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance with Expanded Views | | Junbo Zhang
; Kaisheng Ma
; | | Date: |
1 Jun 2022 | | Abstract: | A data augmentation module is utilized in contrastive learning to transform
the given data example into two views, which is considered essential and
irreplaceable. However, the predetermined composition of multiple data
augmentations brings two drawbacks. First, the artificial choice of
augmentation types brings specific representational invariances to the model,
which have different degrees of positive and negative effects on different
downstream tasks. Treating each type of augmentation equally during training
makes the model learn non-optimal representations for various downstream tasks
and limits the flexibility to choose augmentation types beforehand. Second, the
strong data augmentations used in classic contrastive learning methods may
bring too much invariance in some cases, and fine-grained information that is
essential to some downstream tasks may be lost. This paper proposes a general
method to alleviate these two problems by considering where and what to
contrast in a general contrastive learning framework. We first propose to learn
different augmentation invariances at different depths of the model according
to the importance of each data augmentation instead of learning
representational invariances evenly in the backbone. We then propose to expand
the contrast content with augmentation embeddings to reduce the misleading
effects of strong data augmentations. Experiments based on several baseline
methods demonstrate that we learn better representations for various benchmarks
on classification, detection, and segmentation downstream tasks. | | Source: | arXiv, 2206.00227 | | Services: | Forum | Review | PDF | Favorites | |
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