Generative Active Learning for Image Synthesis Personalization
arxiv(2024)
摘要
This paper presents a pilot study that explores the application of active
learning, traditionally studied in the context of discriminative models, to
generative models. We specifically focus on image synthesis personalization
tasks. The primary challenge in conducting active learning on generative models
lies in the open-ended nature of querying, which differs from the closed form
of querying in discriminative models that typically target a single concept. We
introduce the concept of anchor directions to transform the querying process
into a semi-open problem. We propose a direction-based uncertainty sampling
strategy to enable generative active learning and tackle the
exploitation-exploration dilemma. Extensive experiments are conducted to
validate the effectiveness of our approach, demonstrating that an open-source
model can achieve superior performance compared to closed-source models
developed by large companies, such as Google's StyleDrop. The source code is
available at https://github.com/zhangxulu1996/GAL4Personalization.
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