# AgaMiko/data-augmentation-review

List of useful data augmentation resources. You will find here some not common techniques, libraries, links to GitHub repos, papers, and others.

Repository: https://github.com/AgaMiko/data-augmentation-review
Canonical: https://ross.abutalabs.com/products/data-augmentation-review
License Family: other
Topics: data-augmentation, data-synthesis, data-generation, generative-adversarial-network, review, survey, style-transfer, machine-learning, augmentation-policies, data-augmentations, autoaugment, graph-data-augmentation, image-augmentation, audio-augmentation, nlp-augmentation
Last push: 2024-08-14T11:47:54+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2531, "days_push": 749, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1639, forks 206 (observed 2026-08-28T04:05:15.224578+00:00)

## What it is
A curated list of data augmentation resources covering libraries, GitHub repos, and papers across images, NLP, audio, time series, graphs, and gene expression. It also surveys augmentation policies like AutoAugment and related challenges, workshops, and tutorials.

## Use cases
- find data augmentation libraries for image classification
- learn augmentation techniques for NLP text data
- discover audio augmentation methods for speech models
- find papers on AutoAugment and augmentation policies
- research graph data augmentation approaches
- find resources for augmenting time series data

## When to choose
- you need a survey of augmentation techniques and papers across modalities
- you are researching which augmentation library to adopt
- you want a starting point for learning about data augmentation

## When to avoid
- you need a runnable augmentation tool rather than a reference list
- you need production-ready, maintained software

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-generation, image-processing, audio-processing, nlp
- domain: machine-learning, data-science, computer-vision, tutorials
- platform: cross-platform
- tags: awesome-list, data-augmentation, survey, papers, autoaugment, style-transfer, gan, natural-language-processing, audio

## Member repositories
- AgaMiko/data-augmentation-review (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:15.224578+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 2026-08-30T03:46:25.056309+00:00, confidence not recorded.
  - readme: https://github.com/AgaMiko/data-augmentation-review (fetched 2026-08-28T04:05:15.224578+00:00, sha d695e46efcbe)
- Data as of 2026-08-30T08:39:29.467469+00:00.