JEPA Learns What the Mask Leaves Unrecoverable

arXiv:2609.32481 · cs.CV, cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Linear probe — 11× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Self-Supervised Learning — 6× in this paper · explained in 8 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 4× in this paper
• International Conference on Machine Learning — 4× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Distillation — 3× in this paper · explained in 2 episodes
• Generative AI — 3× in this paper · explained in 2 episodes
• In European Conference on Computer Vision — 3× in this paper
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 1× in this paper
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Linear Probing — 1× in this paper · explained in 2 episodes
• Ontology — 1× in this paper · explained in 2 episodes
• Parameter Identifiability — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Self-Distillation — 1× in this paper · explained in 7 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• BEiT: BERT Pre-Training of Image Transformers
• Revisiting Feature Prediction for Learning Visual Representations from Video
• Structured-Noise Masked Modeling for Video, Audio and Beyond
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• A-JEPA: Joint-Embedding Predictive Architecture Can Listen
• Rethinking JEPA: Compute-Efficient Video SSL with Frozen Teachers
• UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
• iBOT: Image BERT Pre-Training with Online Tokenizer

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