toantm / BGADLLinks
Pytorch implementation of the paper Bayesian Generative Active Deep Learning (ICML 2019).
☆25Updated 6 years ago
Alternatives and similar repositories for BGADL
Users that are interested in BGADL are comparing it to the libraries listed below
Sorting:
- Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"☆80Updated 5 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated 2 years ago
- Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018☆121Updated 5 years ago
- DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional Networks https://arxiv.org/abs/1901.09229☆66Updated 4 years ago
- Code release for Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation (ICML 2019)☆64Updated 6 years ago
- ICML'19: How does Disagreement Help Generalization against Label Corruption?☆22Updated 6 years ago
- Code released for ICML 2019 paper "Bridging Theory and Algorithm for Domain Adaptation".☆140Updated 6 years ago
- Code for "Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers"☆27Updated 3 years ago
- Code for the paper "Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets", ICCV 2019☆27Updated 5 years ago
- The official implementation of paper "Unsupervised Few-Shot Learning via Distribution Shift-based Augmentation"☆26Updated 3 years ago
- Improving Consistency-Based Semi-Supervised Learning with Weight Averaging☆187Updated 6 years ago
- The Ultimate Reference for Out of Distribution Detection with Deep Neural Networks☆118Updated 5 years ago
- The rep for the RotateNetworks in ICPR18, Beijing, China.☆33Updated 7 years ago
- Code for CVPR 2019 paper Label Propagation for Deep Semi-supervised Learning☆117Updated 5 years ago
- Meta-Learning based Noise-Tolerant Training☆124Updated 5 years ago
- Code for paper "Dimensionality-Driven Learning with Noisy Labels" - ICML 2018☆58Updated last year
- ☆13Updated 5 years ago
- Code for: "Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes" and "TaskNorm: Rethinking Batch Norma…☆161Updated 4 years ago
- ☆42Updated 6 years ago
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Updated 4 years ago
- NeurIPS 2019 : Learning to Propagate for Graph Meta-Learning☆36Updated 5 years ago
- Code accompanying the ICML-2018 paper "Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace"☆38Updated 6 years ago
- Open-source code for our paper: Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition☆65Updated 3 years ago
- Code release for "Transferable Normalization: Towards Improving Transferability of Deep Neural Networks" (NeurIPS 2019)☆79Updated 4 years ago
- Code for Unsupervised Learning via Meta-Learning.☆67Updated 4 years ago
- Code release for Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation (ICML 2019)☆92Updated 6 years ago
- Virtual Adversarial Training (VAT) for semi-supervised MNIST written in PyTorch: https://arxiv.org/abs/1704.03976☆25Updated 6 years ago
- Outlier Exposure with Confidence Control for Out-of-Distribution Detection☆71Updated 4 years ago
- Counterfactual Image Generation☆83Updated 6 years ago
- Code for Paper "Incremental Few-Shot Learning with Attention Attractor Networks"☆122Updated 5 years ago