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This project is an AI research implementation library and machine learning research repository. It provides a collection of reference code, illustrative implementations, and open-source research datasets used to verify hypotheses and build upon existing models in artificial intelligence. The repository focuses on scientific research reproduction by translating theoretical findings from published papers into executable code. It includes specialized scientific simulation environments designed to test the behavior of autonomous agents and models within controlled settings. The project covers AI
We release paper and code for SwAV, our new self-supervised method. SwAV pushes self-supervised learning to only 1.2% away from supervised learning on ImageNet with a ResNet-50! It combines online clustering with a multi-crop data augmentation.
A state-of-the-art semi-supervised method for image recognition
Code for the NeurIPS 2017 Paper "Prototypical Networks for Few-shot Learning"
The main features of jakesnell/prototypical-networks are: Advanced Learning.
Projects with overlapping indexed features include: ddtm/caffe — Caffe: a fast open framework for deep learning. deepmind/deepmind-research — This project is an AI research implementation library and machine learning research repository. It provides a… facebookresearch/deepcluster — We release paper and code for SwAV, our new self-supervised method. SwAV pushes self-supervised learning to only 1.2%… facebookresearch/deit — DeiT is a PyTorch vision transformer framework designed for image classification. It implements a transformer-based… facebookresearch/dino — This project is a PyTorch vision transformer framework designed for self-supervised learning. It implements a model… curiousai/mean-teacher — A state-of-the-art semi-supervised method for image recognition.
