benathi / fastswa-semi-supLinks
Improving Consistency-Based Semi-Supervised Learning with Weight Averaging
☆186Updated 6 years ago
Alternatives and similar repositories for fastswa-semi-sup
Users that are interested in fastswa-semi-sup are comparing it to the libraries listed below
Sorting:
- Code for reproducing ICT (published in Neural Networks 2022, and in IJCAI 2019)☆149Updated 2 years ago
- A DIRT-T Approach to Unsupervised Domain Adaptation (ICLR 2018)☆176Updated 7 years ago
- Code repository for the small image experiments our paper 'Self-ensembling for Domain Adaptation'☆194Updated 5 years ago
- Virtual Adversarial Training (VAT) implementation for PyTorch☆295Updated 6 years ago
- ☆171Updated 4 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated 2 years ago
- ☆129Updated 2 years ago
- Pytorch implementation of Virtual Adversarial Training☆134Updated 6 years ago
- Implementation of "Generate To Adapt: Aligning Domains using Generative Adversarial Networks"☆142Updated 6 years ago
- Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018☆121Updated 5 years ago
- Meta-Learning based Noise-Tolerant Training☆126Updated 5 years ago
- Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018☆182Updated 5 years ago
- PyTorch implementation of Temporal Ensembling for Semi-Supervised Learning☆111Updated 6 years ago
- Unofficial PyTorch Implementation of Unsupervised Data Augmentation.☆147Updated 4 years ago
- Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"☆80Updated 5 years ago
- Code for reproducing Manifold Mixup results (ICML 2019)☆494Updated last year
- ☆172Updated 2 years ago
- Code for Paper ''Dual Student: Breaking the Limits of the Teacher in Semi-Supervised Learning'' [ICCV 2019]☆118Updated 5 years ago
- Domain Generalization via Model-Agnostic Learning of Semantic Features☆148Updated 2 years ago
- Learning What and Where to Transfer (ICML 2019)☆248Updated 4 years ago
- Domain agnostic learning with disentangled representations☆147Updated 5 years ago
- Tensorflow codes for ICML2018, Learning Semantic Representations for Unsupervised Domain Adaptation☆110Updated 7 years ago
- [ICCV 2019 oral] Code for Semi-Supervised Learning by Augmented Distribution Alignment☆62Updated 3 years ago
- Reproduction of Momentum Contrast for Unsupervised Visual Representation Learning☆120Updated last year
- Learning Confidence for Out-of-Distribution Detection in Neural Networks☆275Updated 7 years ago
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆354Updated 6 years ago
- ☆351Updated 5 years ago
- Code release for Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation (ICML 2019)☆64Updated 6 years ago
- Official implementation of Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation presented at ICCV 2019.☆163Updated last year
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Updated 4 years ago