library_name: transformers
license: cc-by-nc-4.0
datasets:
- timm/imagenet-22k-wds
I-JEPA Model (Huge, fine-tuned on IN22K)
I-JEPA is a method for self-supervised learning. At a high level, I-JEPA predicts the representations of part of an image from the representations of other parts of the same image:
- without relying on pre-specified invariances to hand-crafted data transformations, which tend to be biased for particular downstream tasks,
- and without having the model fill in pixel-level details, which tend to result in learning less semantically meaningful representations.
How does it work?
As opposed to generative methods that have a pixel decoder, I-JEPA has a predictor that makes predictions in latent space. The predictor in I-JEPA can be seen as a primitive (and restricted) world-model that is able to model spatial uncertainty in a static image from a partially observable context. This world model is semantic in the sense that it predicts high level information about unseen regions in the image, rather than pixel-level details.
We trained a stochastic decoder that maps the I-JEPA predicted representations back in pixel space as sketches. The model correctly captures positional uncertainty and produces high-level object parts with the correct pose (e.g., dog’s head, wolf’s front legs).
Intended uses & limitations
I-JEPA can be used for image classification or feature extraction. This checkpoint in specific is intended for Feature Extraction.
BibTeX entry and citation info
If you use I-JEPA or this code in your work, please cite:
@article{assran2023self,
title={Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture},
author={Assran, Mahmoud and Duval, Quentin and Misra, Ishan and Bojanowski, Piotr and Vincent, Pascal and Rabbat, Michael and LeCun, Yann and Ballas, Nicolas},
journal={arXiv preprint arXiv:2301.08243},
year={2023}
}