amzn / confident-sinkhorn-allocationLinks
Pseudo-labeling for tabular data
☆23Updated last year
Alternatives and similar repositories for confident-sinkhorn-allocation
Users that are interested in confident-sinkhorn-allocation are comparing it to the libraries listed below
Sorting:
- ☆16Updated 3 years ago
- Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty☆143Updated 2 years ago
- ☆44Updated 3 years ago
- C-Mixup for NeurIPS 2022☆73Updated last year
- Official PyTorch implementation of the Fishr regularization for out-of-distribution generalization☆87Updated 3 years ago
- An implementation of the state-of-the-art Deep Active Learning algorithms☆104Updated last year
- Active Learning on a Budget - Opposite Strategies Suit High and Low Budgets☆95Updated 10 months ago
- Weakly Supervised Contrastive Learning☆43Updated 3 years ago
- Code and results accompanying our paper titled RLSbench: Domain Adaptation under Relaxed Label Shift☆35Updated 2 years ago
- Official code for ICML 2022: Open-Sampling: Exploring Out-of-Distribution Data for Re-balancing Long-tailed Datasets☆15Updated 3 years ago
- PyTorch code for Uncertainty-guided Source-free Domain Adaptation☆36Updated 3 years ago
- SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021]☆137Updated 4 years ago
- A PyTorch toolkit with 8 popular deep active learning query methods implemented.☆89Updated 3 years ago
- The Official Repository for CVPR2023 Paper "NICO++: Towards Better Benchmarking for Domain Generalization".☆41Updated 2 years ago
- Repo for the paper: "Agree to Disagree: Diversity through Disagreement for Better Transferability"☆36Updated 2 years ago
- Official code for ICML 2022: Mitigating Neural Network Overconfidence with Logit Normalization☆152Updated 3 years ago
- DISTIL: Deep dIverSified inTeractIve Learning. An active/inter-active learning library built on py-torch for reducing labeling costs.☆154Updated 2 years ago
- On the Importance of Gradients for Detecting Distributional Shifts in the Wild☆56Updated 3 years ago
- Code for ICML 2022 paper "Out-of-distribution Detection with Deep Nearest Neighbors"☆186Updated last year
- Code and results accompanying our paper titled Leveraging Unlabeled Data to Predict Out-of-Distribution Performance at ICLR 2022☆11Updated 2 years ago
- PyTorch implementation of our ECCV 2022 paper "Rethinking Confidence Calibration for Failure Prediction"☆25Updated 2 years ago
- A new mini-batch framework for optimal transport in deep generative models, deep domain adaptation, approximate Bayesian computation, col…☆37Updated 2 years ago
- [CVPR 2022] Pytorch implementation for “Debiased Learning from Naturally Imbalanced Pseudo-Labels”☆100Updated 2 years ago
- Code and results accompanying our paper titled Domain Adaptation under Open Set Label Shift☆31Updated 2 years ago
- [ICLR 2023, ICLR DG oral] PAIR, the optimizer and model selection criteria for OOD Generalization☆52Updated last year
- PyTorch implementation of CIDER (How to exploit hyperspherical embeddings for out-of-distribution detection), ICLR 2023☆62Updated 2 years ago
- Official implementation for "Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition" (I…☆43Updated 2 years ago
- ☆108Updated 2 years ago
- Code release for paper Extremely Simple Activation Shaping for Out-of-Distribution Detection☆54Updated last year
- [ICML 2023] Change is Hard: A Closer Look at Subpopulation Shift☆108Updated 2 years ago