uoguelph-mlrg / confidence_estimationLinks
Learning Confidence for Out-of-Distribution Detection in Neural Networks
☆275Updated 7 years ago
Alternatives and similar repositories for confidence_estimation
Users that are interested in confidence_estimation are comparing it to the libraries listed below
Sorting:
- Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018☆182Updated 5 years ago
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks☆231Updated 6 years ago
- Self-Supervised Learning for OOD Detection (NeurIPS 2019)☆267Updated 4 years ago
- Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction☆223Updated 5 years ago
- Code for reproducing Manifold Mixup results (ICML 2019)☆493Updated last year
- Improving Consistency-Based Semi-Supervised Learning with Weight Averaging☆186Updated 6 years ago
- A simple and effective method for detecting out-of-distribution images in neural networks.☆537Updated 3 years ago
- The Ultimate Reference for Out of Distribution Detection with Deep Neural Networks☆118Updated 5 years ago
- PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning☆353Updated 6 years ago
- Meta-Learning based Noise-Tolerant Training☆126Updated 4 years ago
- Code for the paper "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks".☆346Updated 5 years ago
- Addressing Failure Prediction by Learning Model Confidence☆173Updated 2 years ago
- Open source release of the evaluation benchmark suite described in "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"☆460Updated 6 years ago
- Principled Detection of Out-of-Distribution Examples in Neural Networks☆202Updated 8 years ago
- Unofficial PyTorch Implementation of Unsupervised Data Augmentation.☆147Updated 4 years ago
- Virtual Adversarial Training (VAT) implementation for PyTorch☆295Updated 6 years ago
- ☆129Updated 2 years ago
- Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"☆153Updated 4 years ago
- Pytorch implementation of Virtual Adversarial Training☆134Updated 6 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated 2 years ago
- Wide Residual Networks (WideResNets) in PyTorch☆340Updated 4 years ago
- Variational Adversarial Active Learning (ICCV 2019)☆231Updated 2 years ago
- PyTorch implementation of Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, CVPR 2019.☆139Updated 6 years ago
- Source code for ICLR 2018 Paper: Active Learning for Convolutional Neural Networks: A Core-Set Approach☆272Updated 6 years ago
- TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER☆118Updated 8 years ago
- PyTorch implementation of Temporal Ensembling for Semi-Supervised Learning☆111Updated 6 years ago
- Class-Balanced Loss Based on Effective Number of Samples. CVPR 2019☆612Updated 3 years ago
- NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels☆508Updated 3 years ago
- Robust loss functions for deep neural networks (CVPR 2017)☆91Updated 5 years ago
- Learning What and Where to Transfer (ICML 2019)☆248Updated 4 years ago