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12 Types of JEPA
Since Yann LeCun together with Randall Balestriero released a new paper on JEPA (Joint-Embedding Predictive Architecture), laying out its theory and introducing an efficient practical version called LeJEPA, we figured you might need even more JEPA. Here are 7 recent JEPA variants plus 5 iconic ones:
1. LeJEPA → https://huggingface.co/papers/2511.08544
Explains a full theory for JEPAs, defining the “ideal” JEPA embedding as an isotropic Gaussian, and proposes the SIGReg objective to push JEPA toward this ideal, resulting in practical LeJEPA
2. JEPA-T → https://huggingface.co/papers/2510.00974
A text-to-image model that tokenizes images and captions with a joint predictive Transformer, enhances fusion with cross-attention and text embeddings before training loss, and generates images by iteratively denoising visual tokens conditioned on text
3. Text-JEPA → https://huggingface.co/papers/2507.20491
Converts natural language into first-order logic, with a Z3 solver handling reasoning, enabling efficient, explainable QA with far lower compute than large LLMs
4. N-JEPA (Noise-based JEPA) → https://huggingface.co/papers/2507.15216
Connects self-supervised learning with diffusion-style noise by using noise-based masking and multi-level schedules, especially improving visual classification
5. SparseJEPA → https://huggingface.co/papers/2504.16140
Adds sparse representation learning to make embeddings more interpretable and efficient. It groups latent variables by shared semantic structure using a sparsity penalty while preserving accuracy
6. TS-JEPA (Time Series JEPA) → https://huggingface.co/papers/2509.25449
Adapts JEPA to time-series by learning latent self-supervised representations and predicting future latents for robustness to noise and confounders
Read further below ↓
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