Projects sharing features with SupContrast

This project is a PyTorch vision transformer framework designed for self-supervised learning. It implements a model that trains visual representations using a momentum teacher and self-distillation without the need for labeled data. The library functions as an image feature extractor and visual attention visualizer, allowing for the generation of high-dimensional vectors and the rendering of self-attention maps as heatmaps or videos to analyze model focus. It provides comprehensive tools for downstream vision evaluation, including linear probe classification, k-nearest neighbor categorizatio

moco is a PyTorch implementation of momentum contrast designed for self-supervised visual representation learning. It serves as a research-based framework for extracting high-level image features from unlabeled datasets by maximizing the similarity between different views of the same image. The system utilizes an asymmetric encoder architecture consisting of a fast-learning online encoder and a slow-evolving momentum encoder to stabilize training. It employs a dictionary-based approach that compares query images against a dynamic queue of negative samples to learn distinguishing visual featur

PyTorch implementation of SimSiam https//arxiv.org/abs/2011.10566

PyTorch implementation of SwAV https//arxiv.org/abs/2006.09882

VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.

A simple method to perform semi-supervised learning with limited data.

Code for the paper: "MixMatch - A Holistic Approach to Semi-Supervised Learning" by David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver and Colin Raffel.

Code for Noisy Student Training. https://arxiv.org/abs/1911.04252

This project is a self-supervised contrastive learning framework designed to train deep learning models to learn visual representations from images without using human-provided labels. It provides a system for developing pretrained visual representation models that can be adapted for downstream computer vision tasks. The framework includes tools for semi-supervised image classification, which combines large unlabeled datasets with small labeled sets to improve accuracy. It also features a linear probe evaluation tool to assess the quality of learned image features by training a simple linear

arXiv 2019 "Contrastive Multiview Coding", also contains implementations for MoCo and InstDis

Code for the NeurIPS 2017 Paper "Prototypical Networks for Few-shot Learning"

This project is a PyTorch implementation of a text-to-image transformer. It is a generative AI model designed to map discrete text tokens to image pixels using a transformer network to create visual content from textual descriptions. The system utilizes a discrete VAE image encoder to compress visual data into tokens for transformer processing. It supports classifier-free guidance to adjust the influence of text prompts during inference and includes capabilities for ranking generated images based on their similarity to text prompts. The architecture incorporates sparse attention mechanisms a

This project is a comprehensive framework and toolkit for developing, optimizing, and deploying transformer-based models across multimodal, document intelligence, and natural language processing tasks. It provides a unified neural architecture that processes text, vision, audio, and document layout data through a shared set of weights, enabling researchers and developers to build foundational models that align cross-modal representations. The platform distinguishes itself through advanced training and inference strategies designed for large-scale deep learning. It incorporates specialized mec

This project is a library of pretrained computer vision architectures and backbones for image classification and feature extraction. It serves as a comprehensive model zoo and collection of standardized image encoders, including ResNet, Vision Transformers, and EfficientNet, for use in visual analysis and as backbones for object detection and image segmentation. The library provides a framework for distributed training and evaluation of image models using advanced data augmentation and optimization scripts. It includes a dedicated toolset for converting trained PyTorch vision models into the

Code release of "Learning Transferable Features with Deep Adaptation Networks" (ICML 2015)