Methods › Computer Vision › Generative Models › HiSD

Hierarchical Style Disentanglement

HiSD

2 papers tagged archive 2025-07-28

Introduced by Xinyang Li et al. in Image-to-image Translation via Hierarchical Style Disentanglement

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Hierarchical Style Disentanglement, or HiSD, aims to disentangle different styles in image-to-image translation models. It organizes the labels into a hierarchical structure, where independent tags, exclusive attributes, and disentangled styles are allocated from top to bottom. To make the styles identified to the tags and attributes, the authors carefully redesign the modules, phases, and objectives.

PaperSource

Papers archive 2025-07-28

2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Disentanglement1
Image-to-Image Translation1
Multimodal Unsupervised Image-To-Image Translation1
Translation1

Usage over time archive 2025-07-28

Papers per year tagged with HiSD: 2021 to 2024, peak 1 1 0 2021: 1 paper 2021 2022: 0 papers 2022 2023: 0 papers 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (2 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Generative Models

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