Papers › A Framework For Contrastive Self-Supervised Learning And Designing A New Approach
A Framework For Contrastive Self-Supervised Learning And Designing A New Approach
William Falcon, Kyunghyun Cho
Contrastive self-supervised learning (CSL) is an approach to learn useful representations by solving a pretext task that selects and compares anchor, negative and positive (APN) features from an unlabeled dataset. We present a conceptual framework that characterizes CSL approaches in five aspects (1) data augmentation pipeline, (2) encoder selection, (3) representation extraction, (4) similarity measure, and (5) loss function. We analyze three leading CSL approaches--AMDIM, CPC, and SimCLR--, and show that despite different motivations, they are special cases under this framework. We show the utility of our framework by designing Yet Another DIM (YADIM) which achieves competitive results on CIFAR-10, STL-10 and ImageNet, and is more robust to the choice of encoder and the representation extraction strategy. To support ongoing CSL research, we release the PyTorch implementation of this conceptual framework along with standardized implementations of AMDIM, CPC (V2), SimCLR, BYOL, Moco (V2) and YADIM.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | STL-10 | AMDIM | Percentage correct | 93.80 | #25 of 117 | Archive leaderboard | report |
| Image Classification | STL-10 | YADIM | Percentage correct | 92.15 | #29 of 117 | Archive leaderboard | report |
| Image Classification | STL-10 | CPC† | Percentage correct | 78.36 | #67 of 117 | Archive leaderboard | report |
| Image Classification | STL-10 | Simulated Fixations | Percentage correct | 61 | #103 of 117 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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