jlko / active-testingLinks
Active and Sample-Efficient Model Evaluation
☆24Updated 2 months ago
Alternatives and similar repositories for active-testing
Users that are interested in active-testing are comparing it to the libraries listed below
Sorting:
- Model Patching: Closing the Subgroup Performance Gap with Data Augmentation☆42Updated 4 years ago
- ☆37Updated 4 years ago
- Reusable BatchBALD implementation☆79Updated last year
- (ICML 2021) Mandoline: Model Evaluation under Distribution Shift☆30Updated 4 years ago
- ModelDiff: A Framework for Comparing Learning Algorithms☆59Updated last year
- Last-layer Laplace approximation code examples☆82Updated 3 years ago
- Code for the CVPR 2021 paper: Understanding Failures of Deep Networks via Robust Feature Extraction☆36Updated 3 years ago
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆41Updated 2 years ago
- Figures & code from the paper "Shortcut Learning in Deep Neural Networks" (Nature Machine Intelligence 2020)☆99Updated 3 years ago
- ☆58Updated 3 years ago
- A way to achieve uniform confidence far away from the training data.☆38Updated 4 years ago
- Implementation of the paper "Understanding anomaly detection with deep invertible networks through hierarchies of distributions and featu…☆42Updated 4 years ago
- Combating hidden stratification with GEORGE☆64Updated 4 years ago
- ☆46Updated 2 years ago
- Fine-grained ImageNet annotations☆29Updated 5 years ago
- Gradient Starvation: A Learning Proclivity in Neural Networks☆61Updated 4 years ago
- The official repository for "Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning" paper.☆40Updated 3 years ago
- 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
- ☆38Updated 4 years ago
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆129Updated 3 years ago
- ☆46Updated 4 years ago
- MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space☆41Updated 4 years ago
- [NeurIPS 2020] Coresets for Robust Training of Neural Networks against Noisy Labels☆34Updated 4 years ago
- Code for paper "Can contrastive learning avoid shortcut solutions?" NeurIPS 2021.☆47Updated 3 years ago
- Code for the paper "Calibrating Deep Neural Networks using Focal Loss"☆160Updated last year
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆42Updated last year
- Python implementation for evaluating explanations presented in "On the (In)fidelity and Sensitivity for Explanations" in NeurIPS 2019 for…☆25Updated 3 years ago
- This repository holds code and other relevant files for the NeurIPS 2022 tutorial: Foundational Robustness of Foundation Models.☆71Updated 2 years ago
- Explores the ideas presented in Deep Ensembles: A Loss Landscape Perspective (https://arxiv.org/abs/1912.02757) by Stanislav Fort, Huiyi …☆65Updated 5 years ago