A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization
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Updated
Apr 28, 2024
A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization
The Official Repository for "Generalized OOD Detection: A Survey"
[NeurIPS 2023] RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions
[ECCV'22 Oral] Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes
[ICCV 2021 Oral] Deep Evidential Action Recognition
👽 Out-of-Distribution Detection with PyTorch
[ICCV'23] Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
Feature Space Singularity for Out-of-Distribution Detection. (SafeAI 2021)
We propose a theoretically motivated method, Adversarial Training with informative Outlier Mining (ATOM), which improves the robustness of OOD detection to various types of adversarial OOD inputs and establishes state-of-the-art performance.
ICCV 2023: CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say No
A project to add scalable state-of-the-art out-of-distribution detection (open set recognition) support by changing two lines of code! Perform efficient inferences (i.e., do not increase inference time) and detection without classification accuracy drop, hyperparameter tuning, or collecting additional data.
Robust Out-of-distribution Detection in Neural Networks
The Official Implementation of the ICCV-2021 Paper: Semantically Coherent Out-of-Distribution Detection.
Out-of-distribution detection using the pNML regret. NeurIPS2021
[Findings of EMNLP 2022] Holistic Sentence Embeddings for Better Out-of-Distribution Detection
TensorFlow 2 implementation of the paper Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data (https://arxiv.org/abs/2002.11297).
A project to improve out-of-distribution detection (open set recognition) and uncertainty estimation by changing a few lines of code in your project! Perform efficient inferences (i.e., do not increase inference time) without repetitive model training, hyperparameter tuning, or collecting additional data.
Official PyTorch implementation of MOOD series: (1) MOODv1: Rethinking Out-of-distributionDetection: Masked Image Modeling Is All You Need. (2) MOODv2: Masked Image Modeling for Out-of-Distribution Detection.
Paper of out of distribution detection and generalization
[ICCV'23 Oral] Unmasking Anomalies in Road-Scene Segmentation
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