The dataset viewer is not available because its heuristics could not detect any supported data files. You can try uploading some data files, or configuring the data files location manually.
Pocket JEPA
Pocket JEPA is a small-compute joint-embedding predictive architecture for 8x8 visual worlds. The online network sees a partially hidden image and predicts the latent representation produced by an exponential-moving-average target encoder on the complete image. It never reconstructs pixels and never uses class labels during pretraining.
The experiment measures whether the learned J-space becomes useful under a strict ten-labels-per-class linear-probe budget. An architecture-matched random encoder and a raw-pixel probe are retained as controls. A separate masked-view evaluation tests whether predictive pretraining creates occlusion-tolerant features.
This is a compact JEPA-inspired experiment, not an implementation or reproduction claim for any specific large-scale I-JEPA result.
Verified results
After 260 unlabeled pretraining epochs on 1,347 images, a linear classifier received only ten labels per class:
| Representation | Clean accuracy | Unseen block-mask accuracy |
|---|---|---|
| Pocket JEPA | 89.56% | 85.78% |
| Architecture-matched random encoder | 74.44% | 55.56% |
| Raw pixels | 88.67% | 72.22% |
The deployed encoder has 14,848 parameters. Its training-only predictor has 4,192 parameters. Pocket JEPA improved the masked-view result by 30.22 points over the random encoder and 13.56 points over raw pixels.
Reproduce
uv run python projects/pocket-jepa/train.py
- Downloads last month
- 2