Alternatives and similar repositories for gram-ood-detection

Users that are interested in gram-ood-detection are comparing it to the libraries listed below

• jfc43 / robust-ood-detection
  Robust Out-of-distribution Detection in Neural Networks
  ☆73Updated 3 years ago
• deeplearning-wisc / gradnorm_ood
  On the Importance of Gradients for Detecting Distributional Shifts in the Wild
  ☆56Updated 3 years ago
• megvii-research / FSSD_OoD_Detection
  [SafeAI'21] Feature Space Singularity for Out-of-Distribution Detection.
  ☆79Updated 4 years ago
• rajatkoner08 / oodformer
  This is a code repository for paper OODformer: Out-Of-Distribution Detection Transformer
  ☆41Updated 4 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
• stanislavfort / exploring_the_limits_of_OOD_detection
  Code to replicate the key results from Exploring the Limits of Out-of-Distribution Detection (https://arxiv.org/abs/2106.03004) by Stanis…
  ☆44Updated 3 years ago
• YugeTen / fish
  Official implementation of paper Gradient Matching for Domain Generalization
  ☆123Updated 4 years ago
• prithv1 / DMG
  Code for our ECCV paper -- "Learning to Balance Specificity and Invariance for In and Out of Domain Generalization"
  ☆56Updated 5 years ago
• deeplearning-wisc / MOOD
  Code for CVPR2021 paper: MOOD: Multi-level Out-of-distribution Detection
  ☆38Updated 2 years ago
• SaynaEbrahimi / Remembering-for-the-Right-Reasons
  Official Implementation of Remembering for the Right Reasons (ICLR 2021)
  ☆30Updated 4 years ago
• EvZissel / Residual-Flow
  An implementation of the Residual Flow algorithm for out-of-distribution detection.
  ☆31Updated 3 years ago
• alexrame / fishr
  Official PyTorch implementation of the Fishr regularization for out-of-distribution generalization
  ☆89Updated 3 years ago
• tyler-hayes / REMIND
  PyTorch implementation of the REMIND method from our ECCV-2020 paper "REMIND Your Neural Network to Prevent Catastrophic Forgetting"
  ☆83Updated 2 years ago
• yaohungt / Barlow-Twins-HSIC
  ☆57Updated 4 years ago
• daintlab / confidence-aware-learning
  Confidence-Aware Learning for Deep Neural Networks (ICML2020)
  ☆74Updated 5 years ago
• hendrycks / pre-training
  Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
  ☆100Updated 3 years ago
• belaalb / G2DM
  Generalizing to unseen domains via distribution matching
  ☆72Updated 5 years ago
• stanford-futuredata / sinkhorn-label-allocation
  Sinkhorn Label Allocation is a label assignment method for semi-supervised self-training algorithms. The SLA algorithm is described in fu…
  ☆53Updated 4 years ago
• Vastlab / Reducing-Network-Agnostophobia
  ☆71Updated 7 years ago
• feidfoe / learning-not-to-learn
  [CVPR2019]Learning Not to Learn : An adversarial method to train deep neural networks with biased data
  ☆112Updated 5 years ago
• engkarat / cosine-ood-detector
  Hyperparameter-Free Out-of-Distribution Detection Using Softmax of Scaled Cosine Similarity
  ☆31Updated 2 years ago
• yueatsprograms / uda_release
  Unsupervised Domain Adaptation through Self-Supervision
  ☆79Updated 4 years ago
• brjathu / iTAML
  Official implementation of "iTAML : An Incremental Task-Agnostic Meta-learning Approach". CVPR 2020
  ☆100Updated 2 years ago
• LayneH / self-adaptive-training
  [TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training
  ☆130Updated 4 years ago
• nazim1021 / OOD-detection-using-OECC
  Outlier Exposure with Confidence Control for Out-of-Distribution Detection
  ☆71Updated 4 years ago
• mpezeshki / Gradient_Starvation
  Gradient Starvation: A Learning Proclivity in Neural Networks
  ☆61Updated 4 years ago
• PeymanMorteza / GEM
  ☆19Updated 4 years ago
• hankook / SLA
  Self-supervised Label Augmentation via Input Transformations (ICML 2020)
  ☆107Updated 5 years ago
• alinlab / LfF
  Learning from Failure: Training Debiased Classifier from Biased Classifier (NeurIPS 2020)
  ☆93Updated 5 years ago
• dvirsamuel / DRO-LT
  Code for our paper: Samuel and Chechik, "Distributional Robustness Loss for Long-tail Learning"
  ☆32Updated 3 years ago