# huytransformer/Awesome-Out-Of-Distribution-Detection

Out-of-distribution detection, robustness, and generalization resources. The repository contains a curated list of papers, tutorials, books, videos, articles and open-source libraries etc

Repository: https://github.com/huytransformer/Awesome-Out-Of-Distribution-Detection
Canonical: https://ross.abutalabs.com/products/awesome-out-of-distribution-detection
License: CC0-1.0
License Family: permissive
Topics: awesome, ood-detection, ood-generalization, out-of-distribution, out-of-distribution-detection, out-of-distribution-generalization, robustness, open-world, anomaly-detection, novelty-detection, outlier-detection, open-set-recognition, anomaly, awesome-lists, distribution-shifts, ood-robustness, open-set, open-world-learning, out-of-distribution-robustness
Last push: 2026-04-03T18:05:25+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 75, release rhythm 23, longevity 80
- inputs: {"age_days": 1122, "days_push": 152, "days_rel": 302, "gap_med": 359, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1021, forks 83 (observed 2026-08-28T04:03:15.779419+00:00)

## What it is
A curated awesome-list of papers, tutorials, books, videos, articles, and open-source libraries covering out-of-distribution (OOD) detection, robustness, and generalization in machine learning. It serves as a comprehensive, regularly updated reference hub for OOD research.

## Use cases
- find papers on out-of-distribution detection
- learn about distribution shift robustness
- research open-set recognition methods
- find anomaly detection resources
- survey OOD generalization techniques
- find open-source OOD detection libraries
- prepare a literature review on novelty detection

## When to choose
- you need a curated starting point for OOD research
- you want up-to-date papers and tutorials on distribution shifts
- you are surveying anomaly detection or open-world learning literature

## When to avoid
- you need a runnable OOD detection implementation rather than references
- you want a single maintained software library with an API
- you need production tooling rather than a reading list

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, developer-tools, documentation
- domain: machine-learning, deep-learning, artificial-intelligence, awesome-lists, tutorials
- platform: cross-platform
- tags: awesome-list, ood-detection, ood-robustness, ood-generalization, anomaly-detection, open-set-recognition, distribution-shift, papers, curated-resources

## Member repositories
- huytransformer/Awesome-Out-Of-Distribution-Detection (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:15.779419+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:08:50.633970+00:00, confidence not recorded.
  - readme: https://github.com/huytransformer/Awesome-Out-Of-Distribution-Detection (fetched 2026-08-28T04:03:15.779419+00:00, sha 1feee5be0031)
- Data as of 2026-08-30T08:39:29.467469+00:00.