Papers › Self-Supervised Learning of Pretext-Invariant Representations
Self-Supervised Learning of Pretext-Invariant Representations
Ishan Misra, Laurens van der Maaten
The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations for a large training set of images. Many pretext tasks lead to representations that are covariant with image transformations. We argue that, instead, semantic representations ought to be invariant under such transformations. Specifically, we develop Pretext-Invariant Representation Learning (PIRL, pronounced as "pearl") that learns invariant representations based on pretext tasks. We use PIRL with a commonly used pretext task that involves solving jigsaw puzzles. We find that PIRL substantially improves the semantic quality of the learned image representations. Our approach sets a new state-of-the-art in self-supervised learning from images on several popular benchmarks for self-supervised learning. Despite being unsupervised, PIRL outperforms supervised pre-training in learning image representations for object detection. Altogether, our results demonstrate the potential of self-supervised learning of image representations with good invariance properties.
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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 | imagenet-1k | ResNet50 | ImageNet Top-1 Accuracy | 63.6 | #7 of 14 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet | PIRL | Number of Params | 24M | #117 of 144 | Archive leaderboard | report |
| Self-Supervised Image Classification | ImageNet | PIRL | Top 1 Accuracy | 63.6% | #117 of 144 | Archive leaderboard | report |
| Semi-Supervised Image Classification | ImageNet - 10% labeled data | PIRL (ResNet-50) | Top 5 Accuracy | 83.8% | #60 of 75 | 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
Introduced by this paper: PIRL
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