Abstract
The unique artistic style is crucial to artists' occupational competitiveness, yet prevailing Art Commission Platforms rarely support style-based retrieval. Meanwhile, the fast-growing generative AI techniques aggravate artists' concerns about releasing personal artworks to public platforms. To achieve artistic style-based retrieval without exposing personal artworks, we propose FedStyle, a style-based federated learning crowdsourcing framework. It allows artists to train local style models and share model parameters rather than artworks for collaboration. However, most artists possess a unique artistic style, resulting in severe model drift among them. FedStyle addresses such extreme data heterogeneity by having artists learn their abstract style representations and align with the server, rather than merely aggregating model parameters lacking semantics. Besides, we introduce contrastive learning to meticulously construct the style representation space, pulling artworks with similar styles closer and keeping different ones apart in the embedding space. Extensive experiments on the proposed datasets demonstrate the superiority of FedStyle.
Abstract (translated)
独特的美学风格对艺术家职业竞争力至关重要，然而现行的艺术委员会平台 rarely 支持基于风格的音乐检索。与此同时，快速增长的生成式 AI 技术使艺术家对将个人作品发布到公共平台感到担忧。为了实现基于美学风格的音乐检索而不会泄露个人作品，我们提出了 FedStyle，一种基于风格的分众学习框架。它允许艺术家训练本地风格模型并共享模型参数，而不是为了合作而共享作品。然而，大多数艺术家具有独特的艺术风格，导致他们之间的模型漂移严重。FedStyle 通过让艺术家学习其抽象风格表示来解决这种极端的数据异质性，而不是简单地聚合缺乏语义的数据参数。此外，我们还引入了对比学习来精心构建风格表示空间，将具有相似风格的作品推向更靠近，将不同风格的作品保持在空间中。在提出的数据集上进行的大量实验证明 FedStyle 的优越性。
URL
https://arxiv.org/abs/2404.16336
