StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples

• URL: http://arxiv.org/abs/2410.12757v2
• Date: Sat, 08 Feb 2025 21:45:04 GMT
• Title: StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples
• Authors: Ajay Patel, Jiacheng Zhu, Justin Qiu, Zachary Horvitz, Marianna Apidianaki, Kathleen McKeown, Chris Callison-Burch,
• Abstract summary: Style representations aim to embed texts with similar writing styles closely and texts with different styles far apart, regardless of content. We introduce StyleDistance, a novel approach to training stronger content-independent style embeddings.
• Score: 48.44036251656947
• License:
• Abstract: Style representations aim to embed texts with similar writing styles closely and texts with different styles far apart, regardless of content. However, the contrastive triplets often used for training these representations may vary in both style and content, leading to potential content leakage in the representations. We introduce StyleDistance, a novel approach to training stronger content-independent style embeddings. We use a large language model to create a synthetic dataset of near-exact paraphrases with controlled style variations, and produce positive and negative examples across 40 distinct style features for precise contrastive learning. We assess the quality of our synthetic data and embeddings through human and automatic evaluations. StyleDistance enhances the content-independence of style embeddings, which generalize to real-world benchmarks and outperform leading style representations in downstream applications. Our model can be found at https://huggingface.co/StyleDistance/styledistance .

Related papers

• ReStyle3D: Scene-Level Appearance Transfer with Semantic Correspondences [33.06053818091165]
  ReStyle3D is a framework for scene-level appearance transfer from a single style image to a real-world scene represented by multiple views. It combines explicit semantic correspondences with multi-view consistency to achieve precise and coherent stylization. Our code, pretrained models, and dataset will be publicly released to support new applications in interior design, virtual staging, and 3D-consistent stylization.
  arXiv  Detail & Related papers  (2025-02-14T18:54:21Z)
• StyleBlend: Enhancing Style-Specific Content Creation in Text-to-Image Diffusion Models [10.685779311280266]
  StyleBlend is a method designed to learn and apply style representations from a limited set of reference images. Our approach decomposes style into two components, composition and texture, each learned through different strategies.
  arXiv  Detail & Related papers  (2025-02-13T08:26:54Z)
• Isolating authorship from content with semantic embeddings and contrastive learning [49.15148871877941]
  Authorship has entangled style and content inside. We present a technique to use contrastive learning with additional hard negatives synthetically created using a semantic similarity model. This disentanglement technique aims to distance the content embedding space from the style embedding space, leading to embeddings more informed by style.
  arXiv  Detail & Related papers  (2024-11-27T16:08:46Z)
• Learning Interpretable Style Embeddings via Prompting LLMs [46.74488355350601]
  Style representation learning builds content-independent representations of author style in text. Current style representation learning uses neural methods to disentangle style from content to create style vectors. We use prompting to perform stylometry on a large number of texts to create a synthetic dataset and train human-interpretable style representations.
  arXiv  Detail & Related papers  (2023-05-22T04:07:54Z)
• ALADIN-NST: Self-supervised disentangled representation learning of artistic style through Neural Style Transfer [60.6863849241972]
  We learn a representation of visual artistic style more strongly disentangled from the semantic content depicted in an image. We show that strongly addressing the disentanglement of style and content leads to large gains in style-specific metrics.
  arXiv  Detail & Related papers  (2023-04-12T10:33:18Z)
• Few-shot Font Generation by Learning Style Difference and Similarity [84.76381937516356]
  We propose a novel font generation approach by learning the Difference between different styles and the Similarity of the same style (DS-Font) Specifically, we propose a multi-layer style projector for style encoding and realize a distinctive style representation via our proposed Cluster-level Contrastive Style (CCS) loss.
  arXiv  Detail & Related papers  (2023-01-24T13:57:25Z)
• Generating More Pertinent Captions by Leveraging Semantics and Style on Multi-Source Datasets [56.018551958004814]
  This paper addresses the task of generating fluent descriptions by training on a non-uniform combination of data sources. Large-scale datasets with noisy image-text pairs provide a sub-optimal source of supervision. We propose to leverage and separate semantics and descriptive style through the incorporation of a style token and keywords extracted through a retrieval component.
  arXiv  Detail & Related papers  (2021-11-24T19:00:05Z)
• Multi-Style Transfer with Discriminative Feedback on Disjoint Corpus [9.793194158416854]
  Style transfer has been widely explored in natural language generation with non-parallel corpus. A common shortcoming of existing approaches is the prerequisite of joint annotations across all the stylistic dimensions. We show the ability of our model to control styles across multiple style dimensions while preserving content of the input text.
  arXiv  Detail & Related papers  (2020-10-22T10:16:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.