GarrettLee / label_noise_correctionLinks
Implementation of paper: Making Deep Neural Network Robust to Label Noise: a Loss Correction Approach.
☆23Updated 2 years ago
Alternatives and similar repositories for label_noise_correction
Users that are interested in label_noise_correction are comparing it to the libraries listed below
Sorting:
- Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.☆243Updated 6 years ago
- Pytorch Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.☆176Updated 4 years ago
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Updated 6 years ago
- LaSO: Label-Set Operations networks for multi-label few-shot learning - official implementation☆89Updated last year
- Learning What and Where to Transfer (ICML 2019)☆248Updated 4 years ago
- Meta-Learning based Noise-Tolerant Training☆126Updated 4 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆87Updated 6 years ago
- Adaptive Cross-Modal Few-shot learning OSS code. This is a ServiceNow Research project that was started at Element AI.☆65Updated 3 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆223Updated 5 years ago
- Few shot learning☆155Updated 4 years ago
- This is the PyTorch-0.4.0 implementation of few-shot learning on CIFAR-100 with graph neural networks (GNN)☆88Updated 7 years ago
- clustering☆115Updated 6 years ago
- Code for CVPR 2019 paper Label Propagation for Deep Semi-supervised Learning☆116Updated 5 years ago
- Code for 'Joint Optimization Framework for Learning with Noisy Labels'☆38Updated 6 years ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆127Updated last year
- Boosting Few-Shot Visual Learning with Self-Supervision☆138Updated 5 years ago
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆128Updated 5 years ago
- Code release for Transferable Curriculum for Weakly-Supervised Domain Adaptation (AAAI2019)☆18Updated 5 years ago
- Virtual Adversarial Training (VAT) for semi-supervised MNIST written in PyTorch: https://arxiv.org/abs/1704.03976☆25Updated 6 years ago
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆153Updated 4 years ago
- NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels☆509Updated 3 years ago
- IJCAI 2019 : Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph☆56Updated 5 years ago
- Pytorch implementation of Virtual Adversarial Training☆134Updated 6 years ago
- Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning☆156Updated 6 years ago
- Unofficial PyTorch Implementation of Unsupervised Data Augmentation.☆147Updated 4 years ago
- A PyTorch implementation for Asymmetric Tri-training for Unsupervised Domain Adaptation☆44Updated 7 years ago
- Code release for Universal Domain Adaptation(CVPR 2019)☆279Updated 2 years ago
- "Learning to Discover Novel Visual Categories via Deep Transfer Clustering" by Kai Han, Andrea Vedaldi, Andrew Zisserman (ICCV 2019)☆166Updated 2 years ago
- Pytorch Implementation for CVPR2018 Paper: Learning to Compare: Relation Network for Few-Shot Learning☆250Updated 7 years ago
- pytorch implement for the paper Few-Shot Adversarial Domain Adaptation☆55Updated 2 years ago