google / TrustScore
To Trust Or Not To Trust A Classifier. A measure of uncertainty for any trained (possibly black-box) classifier which is more effective than the classifier's own implied confidence (e.g. softmax probability for a neural network).
☆175Updated 2 years ago
Alternatives and similar repositories for TrustScore:
Users that are interested in TrustScore are comparing it to the libraries listed below
- ☆134Updated 5 years ago
- ☆125Updated 3 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆128Updated 3 years ago
- Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AA…☆75Updated 7 years ago
- OD-test: A Less Biased Evaluation of Out-of-Distribution (Outlier) Detectors (PyTorch)☆62Updated last year
- code release for Representer point Selection for Explaining Deep Neural Network in NeurIPS 2018☆67Updated 3 years ago
- Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotlig…☆148Updated 2 years ago
- Algorithms for abstention, calibration and domain adaptation to label shift.☆36Updated 4 years ago
- Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018☆181Updated 5 years ago
- Modular Python Toolbox for Fairness, Accountability and Transparency Forensics☆77Updated last year
- Code/figures in Right for the Right Reasons☆55Updated 4 years ago
- Implementation of Estimating Training Data Influence by Tracing Gradient Descent (NeurIPS 2020)☆230Updated 3 years ago
- Contains code for the NeurIPS 2019 paper "Practical Deep Learning with Bayesian Principles"☆244Updated 5 years ago
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆127Updated 4 years ago
- Calibration of Convolutional Neural Networks☆162Updated 2 years ago
- A lightweight implementation of removal-based explanations for ML models.☆59Updated 3 years ago
- Supervised Local Modeling for Interpretability☆28Updated 6 years ago
- Code for the paper "Calibrating Deep Neural Networks using Focal Loss"☆160Updated last year
- ☆51Updated 4 years ago
- Outlier Exposure with Confidence Control for Out-of-Distribution Detection☆69Updated 4 years ago
- Reusable BatchBALD implementation☆79Updated last year
- ☆468Updated 9 months ago
- Reliability diagrams, Platt's scaling, isotonic regression☆76Updated 10 years ago
- Combating hidden stratification with GEORGE☆63Updated 3 years ago
- Tools for training explainable models using attribution priors.☆124Updated 4 years ago
- Gold Loss Correction☆87Updated 6 years ago
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks☆227Updated 6 years ago
- Code repository for our paper "Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift": https://arxiv.org/abs/1810.119…☆104Updated last year
- Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'☆60Updated 6 years ago
- The Ultimate Reference for Out of Distribution Detection with Deep Neural Networks☆118Updated 5 years ago