# ycjing/Neural-Style-Transfer-Papers

:pencil2: Neural Style Transfer: A Review

Repository: https://github.com/ycjing/Neural-Style-Transfer-Papers
Canonical: https://ross.abutalabs.com/products/neural-style-transfer-papers
License Family: other
Topics: style-transfer, review
Last push: 2022-02-21T10:11:42+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3430, "days_push": 1654, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1638, forks 258 (observed 2026-08-28T04:05:15.089463+00:00)

## What it is
A curated collection of papers, code links, and pre-trained models accompanying the survey 'Neural Style Transfer: A Review' (TVCG 2019). It serves as a reference index for neural style transfer research rather than a runnable software tool.

## Use cases
- find papers on neural style transfer
- survey of style transfer algorithms
- find pre-trained style transfer models
- research reading list for image stylization
- compare neural style transfer methods
- learn about style transfer techniques

## When to choose
- you need a comprehensive literature review of neural style transfer
- you want links to papers, code, and pre-trained models in one place
- you are writing or benchmarking style transfer research

## When to avoid
- you need a runnable style transfer implementation or framework
- you want production-ready image stylization software
- you need actively maintained code

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: image-processing, machine-learning
- domain: computer-vision, deep-learning, image-processing, tutorials
- platform: cross-platform
- tags: neural-style-transfer, paper-collection, survey, computer-graphics, research-papers

## Member repositories
- ycjing/Neural-Style-Transfer-Papers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:15.089463+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 2026-08-30T03:46:39.090244+00:00, confidence not recorded.
  - readme: https://github.com/ycjing/Neural-Style-Transfer-Papers (fetched 2026-08-28T04:05:15.089463+00:00, sha 4f75e38d8edf)
- Data as of 2026-08-30T08:39:29.467469+00:00.