Search Results for "transfer learning"

i-JEPA (Image Joint-Embedding Predictive Architecture) is a self-supervised learning framework that predicts missing high-level representations rather than reconstructing pixels. A context encoder sees visible regions of an image and predicts target embeddings for masked regions produced by a slowly updated target encoder, focusing learning on semantics instead of texture. This objective sidesteps generative pixel losses and avoids heavy negative sampling, producing features that transfer strongly with linear probes and minimal fine-tuning. ...

TLlib is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms or readily apply existing algorithms. We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.

This repository is a collection of useful jupyter notebooks, code snippets and example JSON files illustrating the use of Reinvent 3.2.