deel-ai / oodeelLinks
Simple, compact, and hackable post-hoc deep OOD detection for already trained tensorflow or pytorch image classifiers.
☆58Updated 2 weeks ago
Alternatives and similar repositories for oodeel
Users that are interested in oodeel are comparing it to the libraries listed below
Sorting:
- 👋 Influenciae is a Tensorflow Toolbox for Influence Functions☆63Updated last year
- Build and train Lipschitz constrained networks: TensorFlow implementation of k-Lipschitz layers☆96Updated 4 months ago
- Documentation☆29Updated last week
- New implementations of old orthogonal layers unlock large scale training.☆17Updated 2 weeks ago
- 👋 Xplique is a Neural Networks Explainability Toolbox☆692Updated 9 months ago
- Open-source framework for uncertainty and deep learning models in PyTorch☆409Updated 3 weeks ago
- Build and train Lipschitz-constrained networks: PyTorch implementation of 1-Lipschitz layers. For TensorFlow/Keras implementation, see ht…☆31Updated 3 weeks ago
- 👽 Out-of-Distribution Detection with PyTorch☆300Updated 3 weeks ago
- ☆37Updated 2 weeks ago
- The net:cal calibration framework is a Python 3 library for measuring and mitigating miscalibration of uncertainty estimates, e.g., by a …☆363Updated 11 months ago
- Reliability diagrams visualize whether a classifier model needs calibration☆153Updated 3 years ago
- MetaQuantus is an XAI performance tool to identify reliable evaluation metrics☆36Updated last year
- Conformal prediction for uncertainty quantification in image segmentation☆23Updated 7 months ago
- Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true cla…☆242Updated 2 years ago
- Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty☆142Updated 2 years ago
- 🐟 CODS - Conformal Object Detection and Segmentation☆14Updated 2 weeks ago
- Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations☆607Updated last week
- Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.☆227Updated this week
- FFCV-SSL Fast Forward Computer Vision for Self-Supervised Learning.☆208Updated last year
- Approximating neural network loss landscapes in low-dimensional parameter subspaces for PyTorch☆336Updated last year
- 👋 Puncc is a python library for predictive uncertainty quantification using conformal prediction.☆336Updated last month
- Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"☆272Updated 3 years ago
- 👋 Overcomplete is a Vision-based SAE Toolbox☆67Updated 3 months ago
- Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".☆112Updated 3 years ago
- An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization☆130Updated last year
- Code for the paper "Calibrating Deep Neural Networks using Focal Loss"☆160Updated last year
- Code for ICML 2022 paper "Out-of-distribution Detection with Deep Nearest Neighbors"☆186Updated last year
- NumPy library for calibration metrics☆73Updated 4 months ago
- Large-scale uncertainty benchmark in deep learning.☆60Updated 2 months ago
- 👋 Code for the paper: "Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis" (NeurIPS 2021)☆30Updated 2 years ago