stanford-futuredata / selection-via-proxyLinks
☆96Updated 4 years ago
Alternatives and similar repositories for selection-via-proxy
Users that are interested in selection-via-proxy are comparing it to the libraries listed below
Sorting:
- Official PyTorch implementation of "Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity" (ICLR'21 Oral)☆103Updated 3 years ago
- Code for Active Learning at The ImageNet Scale. This repository implements many popular active learning algorithms and allows training wi…☆53Updated 3 years ago
- ☆107Updated 2 years ago
- DISTIL: Deep dIverSified inTeractIve Learning. An active/inter-active learning library built on py-torch for reducing labeling costs.☆152Updated 2 years ago
- A list of papers on Active Learning and Uncertainty Estimation for Neural Networks.☆66Updated 5 years ago
- Differentiable Data Augmentation Library☆123Updated 2 years ago
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆128Updated 5 years ago
- Compressing Representations for Self-Supervised Learning☆78Updated 4 years ago
- Robust Out-of-distribution Detection in Neural Networks☆73Updated 3 years ago
- [TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training☆128Updated 3 years ago
- Evaluating AlexNet features at various depths☆40Updated 4 years ago
- [SafeAI'21] Feature Space Singularity for Out-of-Distribution Detection.☆80Updated 4 years ago
- Code for "Supermasks in Superposition"☆124Updated last year
- Code for Which Tasks Should Be Learned Together in Multi-task Learning?☆97Updated 2 years ago
- Official implementation of Auxiliary Learning by Implicit Differentiation [ICLR 2021]☆84Updated 11 months ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated 2 years ago
- Variational Adversarial Active Learning (ICCV 2019)☆230Updated last year
- ☆81Updated 2 years ago
- ☆175Updated 11 months ago
- Source code for ICLR 2018 Paper: Active Learning for Convolutional Neural Networks: A Core-Set Approach☆272Updated 6 years ago
- MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts (ICLR 2022)☆109Updated 2 years ago
- Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018☆181Updated 5 years ago
- ☆129Updated 2 years ago
- Tensorflow implementation of S4L: Self-Supervised Semi-Supervised Learning☆94Updated 5 years ago
- Soft-Label Dataset Distillation and Text Dataset Distillation☆74Updated 2 years ago
- When Does Label Smoothing Help?_pytorch_implementationimp☆124Updated 5 years ago
- Reusable BatchBALD implementation☆79Updated last year
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Updated 4 years ago
- Official PyTorch code for CVPR 2020 paper "Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision"☆34Updated 3 years ago
- ☆40Updated 5 years ago