growth-cadet/activelearning_model
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Jose Mario Pareja PRO
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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 -> Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture (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 -> MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features (2307.12698)
Simultaneously interprets video data - dynamic elements (motion) and static details (content) - using a shared encoder
3. V-JEPA -> Revisiting Feature Prediction for Learning Visual Representations from Video (2404.08471)
Presents vision models trained by predicting future video features, without pretrained image encoders, text, negative sampling, or reconstruction
4. UI-JEPA -> UI-JEPA: Towards Active Perception of User Intent through Onscreen User Activity (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) -> A-JEPA: Joint-Embedding Predictive Architecture Can Listen (2311.15830)
Masks spectrogram patches with a curriculum, encodes them, and predicts hidden representations.
6. S-JEPA -> S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention (2403.11772)
Signal-JEPA is used in EEG analysis. It adds a spatial block-masking scheme and three lightweight downstream classifiers
7. TI-JEPA -> TI-JEPA: An Innovative Energy-based Joint Embedding Strategy for Text-Image Multimodal Systems (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 👇
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
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 -> Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture (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 -> MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features (2307.12698)
Simultaneously interprets video data - dynamic elements (motion) and static details (content) - using a shared encoder
3. V-JEPA -> Revisiting Feature Prediction for Learning Visual Representations from Video (2404.08471)
Presents vision models trained by predicting future video features, without pretrained image encoders, text, negative sampling, or reconstruction
4. UI-JEPA -> UI-JEPA: Towards Active Perception of User Intent through Onscreen User Activity (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) -> A-JEPA: Joint-Embedding Predictive Architecture Can Listen (2311.15830)
Masks spectrogram patches with a curriculum, encodes them, and predicts hidden representations.
6. S-JEPA -> S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention (2403.11772)
Signal-JEPA is used in EEG analysis. It adds a spatial block-masking scheme and three lightweight downstream classifiers
7. TI-JEPA -> TI-JEPA: An Innovative Energy-based Joint Embedding Strategy for Text-Image Multimodal Systems (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 👇
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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Introducing Training Cluster as a Service - a new collaboration with NVIDIA
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