Alternatives and similar repositories for Confident_classifier:

Users that are interested in Confident_classifier are comparing it to the libraries listed below

• ashafaei / out-of-distribution-detection
  The Ultimate Reference for Out of Distribution Detection with Deep Neural Networks
  ☆118Updated 5 years ago
• ShiyuLiang / odin-pytorch
  Principled Detection of Out-of-Distribution Examples in Neural Networks
  ☆201Updated 7 years ago
• hendrycks / ss-ood
  Self-Supervised Learning for OOD Detection (NeurIPS 2019)
  ☆266Updated 3 years ago
• hendrycks / error-detection
  A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
  ☆227Updated 6 years ago
• pokaxpoka / deep_Mahalanobis_detector
  Code for the paper "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks".
  ☆345Updated 5 years ago
• uoguelph-mlrg / confidence_estimation
  Learning Confidence for Out-of-Distribution Detection in Neural Networks
  ☆273Updated 6 years ago
• nazim1021 / OOD-detection-using-OECC
  Outlier Exposure with Confidence Control for Out-of-Distribution Detection
  ☆69Updated 4 years ago
• ashafaei / OD-test
  OD-test: A Less Biased Evaluation of Out-of-Distribution (Outlier) Detectors (PyTorch)
  ☆62Updated last year
• mmazeika / glc
  Gold Loss Correction
  ☆87Updated 6 years ago
• ricvolpi / generalize-unseen-domains
  Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018
  ☆121Updated 4 years ago
• benathi / fastswa-semi-sup
  Improving Consistency-Based Semi-Supervised Learning with Weight Averaging
  ☆186Updated 6 years ago
• perrying / realistic-ssl-evaluation-pytorch
  Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"
  ☆80Updated 4 years ago
• LiJunnan1992 / MLNT
  Meta-Learning based Noise-Tolerant Training
  ☆123Updated 4 years ago
• PaulAlbert31 / LabelNoiseCorrection
  Official Implementation of ICML 2019 Unsupervised label noise modeling and loss correction
  ☆222Updated 4 years ago
• Newbeeer / L_DMI
  Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"
  ☆119Updated last year
• google-research / remixmatch
  ☆129Updated 2 years ago
• YU1ut / Ensemble-of-Leave-out-Classifiers
  Code for "Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers"
  ☆27Updated 3 years ago
• ildoonet / unsupervised-data-augmentation
  Unofficial PyTorch Implementation of Unsupervised Data Augmentation.
  ☆146Updated 4 years ago
• srebuffi / residual_adapters
  ☆170Updated 4 years ago
• udibr / noisy_labels
  TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER
  ☆118Updated 7 years ago
• brjathu / iTAML
  Official implementation of "iTAML : An Incremental Task-Agnostic Meta-learning Approach". CVPR 2020
  ☆97Updated last year
• yulongwang12 / visual-attribution
  Pytorch Implementation of recent visual attribution methods for model interpretability
  ☆145Updated 5 years ago
• AlanChou / Truncated-Loss
  PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018
  ☆126Updated 5 years ago
• danieltan07 / learning-to-reweight-examples
  PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning
  ☆353Updated 5 years ago
• mtoneva / example_forgetting
  ☆174Updated 8 months ago
• chenpf1025 / noisy_label_understanding_utilizing
  ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
  ☆91Updated 4 years ago
• 9310gaurav / virtual-adversarial-training
  Pytorch implementation of Virtual Adversarial Training
  ☆134Updated 5 years ago
• google-research / s4l
  Tensorflow implementation of S4L: Self-Supervised Semi-Supervised Learning
  ☆94Updated 5 years ago
• Vastlab / Reducing-Network-Agnostophobia
  ☆70Updated 6 years ago
• feidfoe / learning-not-to-learn
  [CVPR2019]Learning Not to Learn : An adversarial method to train deep neural networks with biased data
  ☆111Updated 4 years ago