rakhimovv / tcavLinks
Quantitative Testing with Concept Activation Vectors in PyTorch
☆43Updated 6 years ago
Alternatives and similar repositories for tcav
Users that are interested in tcav are comparing it to the libraries listed below
Sorting:
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆128Updated 4 years ago
- Python implementation for evaluating explanations presented in "On the (In)fidelity and Sensitivity for Explanations" in NeurIPS 2019 for…☆25Updated 3 years ago
- Full-gradient saliency maps☆212Updated 2 years ago
- ☆112Updated 2 years ago
- Pytorch implementation of Google TCAV☆10Updated 6 years ago
- code release for the paper "On Completeness-aware Concept-Based Explanations in Deep Neural Networks"☆54Updated 3 years ago
- Original dataset release for CIFAR-10H☆82Updated 5 years ago
- ☆66Updated 5 years ago
- SmoothGrad implementation in PyTorch☆172Updated 4 years ago
- Towards Automatic Concept-based Explanations☆161Updated last year
- Explaining Image Classifiers by Counterfactual Generation☆28Updated 3 years ago
- ☆51Updated 5 years ago
- Information Bottlenecks for Attribution☆83Updated 2 years ago
- This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpreta…☆375Updated 3 years ago
- OD-test: A Less Biased Evaluation of Out-of-Distribution (Outlier) Detectors (PyTorch)☆62Updated last year
- Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.☆175Updated 2 years ago
- Figures & code from the paper "Shortcut Learning in Deep Neural Networks" (Nature Machine Intelligence 2020)☆101Updated 3 years ago
- Addressing Failure Prediction by Learning Model Confidence☆173Updated 2 years ago
- ☆69Updated 6 years ago
- Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning (http://jmlr.org/papers/v20/19-033.html)☆89Updated 9 months ago
- [CVPR2019]Learning Not to Learn : An adversarial method to train deep neural networks with biased data☆112Updated 5 years ago
- A way to achieve uniform confidence far away from the training data.☆38Updated 4 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆129Updated 4 years ago
- Detect model's attention☆168Updated 5 years ago
- reference implementation for "explanations can be manipulated and geometry is to blame"☆37Updated 3 years ago
- Robust Out-of-distribution Detection in Neural Networks☆73Updated 3 years ago
- This is the pytorch implementation of the paper - Axiomatic Attribution for Deep Networks.☆188Updated 3 years ago
- Self-Supervised Learning for OOD Detection (NeurIPS 2019)☆267Updated 4 years ago
- Code for the paper "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks".☆348Updated 6 years ago
- Pytorch implementation of various neural network interpretability methods☆118Updated 3 years ago