Papers › IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised...
IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature Representation
Sonal Kumar; Anirudh Phukan, Arijit Sur
Self-supervised learning with a contrastive batch approach has become a powerful tool for representation learning in computer vision. The performance of downstream tasks is proportional to the quality of visual features learned while self-supervised pre-training. The existing contrastive batch approaches heavily depend on data augmentation to learn latent information from unlabelled datasets. We argue that introducing the dataset’s intra-class variation in a contrastive batch approach improves visual representation quality further. In this paper, we propose a novel self-supervised learning approach named Iterative Pseudo-supervised Contrastive Learning (IPCL), which utilizes a balanced combination of image augmentations and pseudo-class information to improve the visual representation iteratively. Experimental results illustrate that our proposed method surpasses the baseline self-supervised method with the batch contrastive approach. It improves the visual representation quality over multiple datasets, leading to better performance on the downstream unsupervised image classification task.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Contrastive Learning | CIFAR-10 | IPCL (ResNet18) | Accuracy (Top-1) | 84.77 | #1 of 1 | Archive leaderboard | report |
| Contrastive Learning | STL-10 | IPCL (ResNet18) | Accuracy (Top-1) | 85.55 | #1 of 1 | Archive leaderboard | report |
| Unsupervised Image Classification | CIFAR-10 | IPCL (ResNet18) | Accuracy | 88.81 | #9 of 9 | Archive leaderboard | report |
| Unsupervised Image Classification | STL-10 | IPCL (ResNet18) | Accuracy | 80.91 | #9 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.
Methods
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