Experiments in Joint Embedding Predictive Architectures (JEPAs).
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Updated
Jan 5, 2024 - Python
Experiments in Joint Embedding Predictive Architectures (JEPAs).
A tiny, fully-reproducible JEPA world model that learns the physics of a bouncing DVD logo in representation space, dreams its future, and detects anomalies. Trains on a CPU in ~10s. Interactive browser demo. CA: 0x42bef487C250dd054035d5E8d69C12549d1F7Ba3
A toolkit for research on multimodal representation learning
A clean, from-scratch PyTorch implementation of I-JEPA trained on STL-10. Built to benchmark representation-space vs. pixel-space prediction against MAE.
A simple and efficient implementation of Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture (I-JEPA)
A research-oriented project focusing on the practical application of the I-JEPA for instance level classification on multi-class images.
Comparative analysis of monocular depth estimation methods (ResNet-50, frozen Stable Diffusion UNet, I-JEPA, SD+I-JEPA fusion, DepthAnything V2) with robustness evaluation under fog, blur, and low-light on NYU Depth V2.
JEPA-guided continuation for improving black-box transfer of released dSVA adversarial generators
Reimplement the I-JEPA paper from scratch with minimum necessary code.
Retrieve Steam games with similar store banners, with Meta AI's I-JEPA.
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