hyj378

hyj378's Stars

• google-research/google-research

  Google Research

  Language:Jupyter Notebook34k7521.3k7.8k

• google-deepmind/deepmind-research

  This repository contains implementations and illustrative code to accompany DeepMind publications

  Language:Jupyter Notebook13.1k3253212.6k

• open-mmlab/mmselfsup

  OpenMMLab Self-Supervised Learning Toolbox and Benchmark

  Language:Python3.2k45279428

• sthalles/SimCLR

  PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations

  Language:Jupyter Notebook2.2k2058460

• google/active-learning

  Language:Python1.1k340206

• vahidk/tfrecord

  Standalone TFRecord reader/writer with PyTorch data loaders

  Language:Python857864106

• salesforce/PCL

  PyTorch code for "Prototypical Contrastive Learning of Unsupervised Representations"

  Language:Python565172583

• leftthomas/SimCLR

  A PyTorch implementation of SimCLR based on ICML 2020 paper "A Simple Framework for Contrastive Learning of Visual Representations"

  Language:Python535736111

• facebookresearch/suncet

  Code to reproduce the results in the FAIR research papers "Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples" https://arxiv.org/abs/2104.13963 and "Supervision Accelerates Pre-training in Contrastive Semi-Supervised Learning of Visual Representations" https://arxiv.org/abs/2006.10803

  Language:Python486123067

• wgrathwohl/JEM

  Project site for "Your Classifier is Secretly an Energy-Based Model and You Should Treat it Like One"

  Language:Python415151163

• haarnoja/softqlearning

  Reinforcement Learning with Deep Energy-Based Policies

  Language:Python411171194

• htdt/hyp_metric

  Hyperbolic Vision Transformers: Combining Improvements in Metric Learning | Official repository

  Language:Python1856820

• PatrickZH/Awesome-Coreset-Selection

  Awesome coreset/core-set/subset/sample selection works.

  1566011

• Rhyssiyan/DER-ClassIL.pytorch

  The official PyTorch code for 'DER: Dynamically Expandable Representation for Class Incremental Learning' accepted by CVPR2021

  Language:Python15382320

• ivclab/CPG

  Steven C. Y. Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, and Chu-Song Chen, "Compacting, Picking and Growing for Unforgetting Continual Learning," Thirty-third Conference on Neural Information Processing Systems, NeurIPS 2019

  Language:Python1199922

• Impression2805/CVPR21_PASS

  PyTorch implementation of our CVPR2021 (oral) paper "Prototype Augmentation and Self-Supervision for Incremental Learning"

  Language:Python10341020

• mingkai-zheng/SimMatch

  SimMatch: Semi-supervised Learning with Similarity Matching

  Language:Python9041110

• ayulockin/SwAV-TF

  TensorFlow implementation of "Unsupervised Learning of Visual Features by Contrasting Cluster Assignments".

  Language:Jupyter Notebook855912

• BIT-DA/EADA

  [AAAI 2022] Official Implementation of Active Learning for Domain Adaptation: An Energy-based Approach https://arxiv.org/abs/2112.01406

  Language:Python771212

• PrateekMunjal/TorchAL

  Official implementation of our paper: Towards Robust and Reproducible Active Learning using Neural Networks, accepted at CVPR 2022.

  Language:Jupyter Notebook64337

• baharanm/craig

  Data-efficient Training of Machine Learning Models

  Language:Python581424

• razvancaramalau/Sequential-GCN-for-Active-Learning

  Language:Python5021017

• yj-zhou/Feature_Encoding_with_AutoEncoders_for_Weakly-supervised_Anomaly_Detection

  Language:Python411010

• siyuhuang/TOD

  PyTorch Implementation of Temporal Output Discrepancy for Active Learning, ICCV 2021

  Language:Python39284

• virajprabhu/CLUE

  PyTorch code for Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings (ICCV 2021)

  Language:Jupyter Notebook39253

• LayneH/LEWEL

  [CVPR2022] Official Implementation of the paper 'Learning Where to Learn in Cross-View Self-Supervised Learning'

  Language:Python25243

• google-research/fnc

  Language:Python24424

• dirichletcal/experiments_neurips

  Language:Jupyter Notebook21215

• kamwoh/DPN

  [IJCAI 2020] This is an official code implementation for Deep Polarized Network for Supervised Learning of Accurate Binary Hashing Codes.

  Language:Python10401

• SAWassermann/RAL

  RAL - Reinforced stream-based Active Learning

  Language:Python9203