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ValerioL29
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12 Types of JEPA JEPA, or Joint Embedding Predictive Architecture, is an approach to building AI models introduced by Yann LeCun. It differs from transformers by predicting the representation of a missing or future part of the input, rather than the next token or pixel. This encourages conceptual understanding, not just low-level pattern matching. So JEPA allows teaching AI to reason abstractly. Here are 12 types of JEPA you should know about: 1. I-JEPA -> https://huggingface.co/papers/2301.08243 A non-generative, self-supervised learning framework designed for processing images. It works by masking parts of the images and then trying to predict those masked parts 2. MC-JEPA -> https://huggingface.co/papers/2307.12698 Simultaneously interprets video data - dynamic elements (motion) and static details (content) - using a shared encoder 3. V-JEPA -> https://huggingface.co/papers/2404.08471 Presents vision models trained by predicting future video features, without pretrained image encoders, text, negative sampling, or reconstruction 4. UI-JEPA -> https://huggingface.co/papers/2409.04081 Masks unlabeled UI sequences to learn abstract embeddings, then adds a fine-tuned LLM decoder for intent prediction. 5. Audio-based JEPA (A-JEPA) -> https://huggingface.co/papers/2311.15830 Masks spectrogram patches with a curriculum, encodes them, and predicts hidden representations. 6. S-JEPA -> https://huggingface.co/papers/2403.11772 Signal-JEPA is used in EEG analysis. It adds a spatial block-masking scheme and three lightweight downstream classifiers 7. TI-JEPA -> https://huggingface.co/papers/2503.06380 Text-Image JEPA uses self-supervised, energy-based pre-training to map text and images into a shared embedding space, improving cross-modal transfer to downstream tasks Find more types below ๐ Also, explore the basics of JEPA in our article: https://www.turingpost.com/p/jepa If you liked it, subscribe to the Turing Post: https://www.turingpost.com/subscribe
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