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11 Types of JEPA
Since Meta released the newest V-JEPA 2 this week, we thought it's a good time to revisit a few other interesting JEPA variants. JEPA, or Joint Embedding Predictive Architecture, a self-supervised learning framework that predicts the latent representation of a missing part of the input.
Here are 11 JEPA types that you should know about:
1. V-JEPA 2 -> https://huggingface.co/papers/2506.09985
Trained on 1M+ hours of internet videos and a little bit of robot interaction data, V-JEPA 2 can watch, understand, answer questions, and help robots plan and act in physical world
2. Time-Series-JEPA (TS-JEPA) -> https://huggingface.co/papers/2406.04853
It's a time-series predictive model that learns compact, meaningful representations. A self-supervised semantic actor then uses them to generate control commands without raw data
3. Denoising JEPA (D-JEPA) -> https://huggingface.co/papers/2410.03755
Combines JEPA with diffusion techniques. By treating JEPA as masked image modeling and next-token prediction, D-JEPA generates data auto-regressively, incorporating diffusion and flow-matching losses
4. CNN-JEPA -> https://huggingface.co/papers/2408.07514
This SSL approach applies JEPA idea to CNNs using a sparse encoder, depthwise separable convolutions, and improved masking. On ImageNet-100, CNN-JEPA outperforms I-JEPA with 73.3% accuracy
5. Stem-JEPA -> https://huggingface.co/papers/2408.02514
Identifies instrument stems by mapping mixes and stems into a shared space using an encoder and predictor. It captures timbre, harmony, and rhythm for tasks like stem retrieval, alignment, and genre or key estimation
6. DMT-JEPA (Discriminative Masked Targets JEPA) -> https://huggingface.co/papers/2405.17995
Improves discriminative power by generating masked targets from semantically similar neighboring patches and uses lightweight cross-attention for aggregation
Read further below👇
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