Papers › Self-Supervised Classification Network
Self-Supervised Classification Network
Elad Amrani, Leonid Karlinsky, Alex Bronstein
We present Self-Classifier -- a novel self-supervised end-to-end classification learning approach. Self-Classifier learns labels and representations simultaneously in a single-stage end-to-end manner by optimizing for same-class prediction of two augmented views of the same sample. To guarantee non-degenerate solutions (i.e., solutions where all labels are assigned to the same class) we propose a mathematically motivated variant of the cross-entropy loss that has a uniform prior asserted on the predicted labels. In our theoretical analysis, we prove that degenerate solutions are not in the set of optimal solutions of our approach. Self-Classifier is simple to implement and scalable. Unlike other popular unsupervised classification and contrastive representation learning approaches, it does not require any form of pre-training, expectation-maximization, pseudo-labeling, external clustering, a second network, stop-gradient operation, or negative pairs. Despite its simplicity, our approach sets a new state of the art for unsupervised classification of ImageNet; and even achieves comparable to state-of-the-art results for unsupervised representation learning. Code is available at https://github.com/elad-amrani/self-classifier.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Self-Supervised Image Classification | ImageNet | Self-Classifier (ResNet-50) | Number of Params | 24M | #87 of 144 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet | Self-Classifier (ResNet-50) | Top 1 Accuracy | 74.2% | #87 of 144 | Archive leaderboard | report |
| Unsupervised Image Classification | ImageNet | Self-Classifier (ResNet-50) | ARI | 29.5 | #5 of 9 | Archive leaderboard | report |
| Unsupervised Image Classification | ImageNet | Self-Classifier (ResNet-50) | Accuracy (%) | 41.1 | #5 of 9 | 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.
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