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Robust Contrastive Learning Using Negative Samples with Diminished Semantics. (arXiv:2110.14189v2 [cs.CV] UPDATED)
Jan. 4, 2022, 9:10 p.m. | Songwei Ge, Shlok Mishra, Haohan Wang, Chun-Liang Li, David Jacobs
cs.CV updates on arXiv.org arxiv.org
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 …
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