kkirchheim / pytorch-ood
π½ Out-of-Distribution Detection with PyTorch
β276Updated last week
Alternatives and similar repositories for pytorch-ood:
Users that are interested in pytorch-ood are comparing it to the libraries listed below
- Benchmarking Generalized Out-of-Distribution Detectionβ922Updated 2 months ago
- β406Updated 3 years ago
- Code for ICML 2022 paper "Out-of-distribution Detection with Deep Nearest Neighbors"β179Updated 7 months ago
- The Official Repository for "Generalized OOD Detection: A Survey"β451Updated 2 years ago
- An implementation of the BADGE batch active learning algorithm.β202Updated 8 months ago
- This is a toolbox for Deep Active Learning, an extension from previous work https://github.com/ej0cl6/deep-active-learning (DeepAL toolboβ¦β174Updated 9 months ago
- Active Learning on a Budget - Opposite Strategies Suit High and Low Budgetsβ88Updated 3 months ago
- Code for the paper "Calibrating Deep Neural Networks using Focal Loss"β160Updated last year
- Open-source framework for uncertainty and deep learning models in PyTorchβ349Updated this week
- Domain adaptation made easy. Fully featured, modular, and customizable.β365Updated 2 years ago
- Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertaintyβ134Updated last year
- An implementation of the state-of-the-art Deep Active Learning algorithmsβ102Updated last year
- DISTIL: Deep dIverSified inTeractIve Learning. An active/inter-active learning library built on py-torch for reducing labeling costs.β147Updated 2 years ago
- Code for the paper "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks".β343Updated 5 years ago
- This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"β454Updated last year
- CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances (NeurIPS 2020)β280Updated last year
- Reliability diagrams visualize whether a classifier model needs calibrationβ145Updated 3 years ago
- Out-of-distribution detection, robustness, and generalization resources. The repository contains a curated list of papers, tutorials, booβ¦β876Updated 3 months ago
- Official repo for our ICLR 22 Oral paper: "Open-Set Recognition: a Good Closed-Set Classifier is All You Need?"β279Updated 2 years ago
- A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.β558Updated last year
- A project to add scalable state-of-the-art out-of-distribution detection (open set recognition) support by changing two lines of code! Peβ¦β75Updated 2 years ago
- Awesome Active Learning Paper Listβ142Updated 9 months ago
- Compare neural networks by their feature similarityβ352Updated last year
- source code for ICLR'22 paper "VOS: Learning What You Donβt Know by Virtual Outlier Synthesis"β310Updated last year
- Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"β271Updated 2 years ago
- Awesome coreset/core-set/subset/sample selection works.β172Updated 7 months ago
- A clean and simple data loading library for Continual Learningβ424Updated last year
- The net:cal calibration framework is a Python 3 library for measuring and mitigating miscalibration of uncertainty estimates, e.g., by a β¦β353Updated 6 months ago
- Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".β111Updated 2 years ago
- Reduce end to end training time from days to hours (or hours to minutes), and energy requirements/costs by an order of magnitude using coβ¦β327Updated last year