peisuke / MomentumContrast.pytorch
Reproduction of Momentum Contrast for Unsupervised Visual Representation Learning
☆120Updated 9 months ago
Alternatives and similar repositories for MomentumContrast.pytorch:
Users that are interested in MomentumContrast.pytorch are comparing it to the libraries listed below
- Auto-Encoding Transformations (AETv1), CVPR 2019☆108Updated 5 years ago
- Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning☆156Updated 5 years ago
- Unofficial PyTorch Implementation of Unsupervised Data Augmentation.☆147Updated 4 years ago
- Pytorch Implementation for CVPR2018 Paper: Learning to Compare: Relation Network for Few-Shot Learning☆249Updated 7 years ago
- EnAET: Self-Trained Ensemble AutoEncoding Transformations for Semi-Supervised Learning☆81Updated last year
- Meta-Learning based Noise-Tolerant Training☆124Updated 4 years ago
- Unofficial implementation with pytorch DistributedDataParallel for "MoCo: Momentum Contrast for Unsupervised Visual Representation Learni…☆148Updated 5 years ago
- ☆129Updated 2 years ago
- Code for Unsupervised Embedding Learning via Invariant and Spreading Instance Feature☆210Updated 5 years ago
- ☆132Updated 4 years ago
- PyTorch implementation of “Negative Margin Matters: Understanding Margin in Few-shot Classification”☆150Updated 4 years ago
- Few shot learning☆155Updated 3 years ago
- ☆170Updated 4 years ago
- Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.☆242Updated 6 years ago
- The implementation of "Self-Supervised Generalisation with Meta Auxiliary Learning" [NeurIPS 2019].☆174Updated 3 years ago
- Improving Consistency-Based Semi-Supervised Learning with Weight Averaging☆186Updated 6 years ago
- The implementation of https://papers.nips.cc/paper/7352-tadam-task-dependent-adaptive-metric-for-improved-few-shot-learning . TADAM is a …☆106Updated 2 years ago
- Boosting Few-Shot Visual Learning with Self-Supervision☆137Updated 5 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆222Updated 4 years ago
- Code for reproducing ICT (published in Neural Networks 2022, and in IJCAI 2019)☆145Updated 2 years ago
- Self-Supervised Representation Learning by Rotation Feature Decoupling☆96Updated 5 years ago
- LaSO: Label-Set Operations networks for multi-label few-shot learning - official implementation☆87Updated last year
- Self-supervised Label Augmentation via Input Transformations (ICML 2020)☆106Updated 4 years ago
- SSL-FEW-SHOT☆172Updated 5 years ago
- Joint Optimization Framework for Learning with Noisy Labels☆45Updated 7 years ago
- Learning What and Where to Transfer (ICML 2019)☆248Updated 4 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated last year
- Pytorch implementation for "Open Compound Domain Adaptation" (CVPR 2020 ORAL)☆139Updated 3 years ago
- Code for Paper ''Dual Student: Breaking the Limits of the Teacher in Semi-Supervised Learning'' [ICCV 2019]☆118Updated 4 years ago
- (ICCV'19 Best Paper Nomination) Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation☆187Updated 5 years ago