Trustworthy-ML-Lab / Label-free-CBMLinks
[ICLR 23] A new framework to transform any neural networks into an interpretable concept-bottleneck-model (CBM) without needing labeled concept data
☆113Updated last year
Alternatives and similar repositories for Label-free-CBM
Users that are interested in Label-free-CBM are comparing it to the libraries listed below
Sorting:
- Code for the paper "Post-hoc Concept Bottleneck Models". Spotlight @ ICLR 2023☆84Updated last year
- [ICLR 23 spotlight] An automatic and efficient tool to describe functionalities of individual neurons in DNNs☆55Updated last year
- CVPR 2023: Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification☆98Updated last year
- [ICML 2023] Change is Hard: A Closer Look at Subpopulation Shift☆110Updated 2 years ago
- Concept Bottleneck Models, ICML 2020☆218Updated 2 years ago
- Repository for our NeurIPS 2022 paper "Concept Embedding Models", our NeurIPS 2023 paper "Learning to Receive Help", and our ICML 2025 pa…☆69Updated 2 weeks ago
- Implementation of Concept-level Debugging of Part-Prototype Networks☆12Updated 2 years ago
- SpuCo is a Python package developed to further research to address spurious correlations.☆24Updated 9 months ago
- ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition☆40Updated 2 years ago
- Code for the paper "A Whac-A-Mole Dilemma Shortcuts Come in Multiples Where Mitigating One Amplifies Others"☆49Updated last year
- [NeurIPS 24] A new training and evaluation framework for learning interpretable deep vision models and benchmarking different interpretab…☆22Updated 4 months ago
- A collection of model transferability estimation methods.☆28Updated last year
- Sparse Linear Concept Embeddings☆115Updated 6 months ago
- ☆23Updated last year
- Official code of "Discover and Mitigate Unknown Biases with Debiasing Alternate Networks" (ECCV 2022)☆25Updated 2 years ago
- Code for Continuously Changing Corruptions (CCC) benchmark + evaluation☆39Updated last year
- ☆43Updated 11 months ago
- This is the official implementation of the Concept Discovery Models paper.☆15Updated 2 years ago
- Code for the paper Visual Explanations of Image–Text Representations via Multi-Modal Information Bottleneck Attribution☆60Updated last year
- (ICML 2023) Discover and Cure: Concept-aware Mitigation of Spurious Correlation☆42Updated last year
- ☆68Updated 2 years ago
- [NeurIPS 2023, ICMI 2023] Quantifying & Modeling Multimodal Interactions☆82Updated 11 months ago
- Test-Time Adaptation via Conjugate Pseudo-Labels☆42Updated 2 years ago
- PyTorch implementation of CIDER (How to exploit hyperspherical embeddings for out-of-distribution detection), ICLR 2023☆63Updated 2 years ago
- [CVPR 2022] HINT: Hierarchical Neuron Concept Explainer☆20Updated 2 years ago
- Repository for research works and resources related to model reprogramming <https://arxiv.org/abs/2202.10629>☆62Updated last month
- PyTorch implementation of MCM (Delving into out-of-distribution detection with vision-language representations), NeurIPS 2022☆89Updated last year
- Official code for "Probabilistic Concept Bottleneck Models (ICML 2023)"☆17Updated 2 years ago
- Code for the paper: Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery. ECCV 2024.☆49Updated 11 months ago
- Analysis of evidential models☆14Updated 2 years ago