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 this https URL

该研究提出了一项旨在将传统上应用于判别模型背景下的主动学习方法应用于生成模型的试点研究，重点关注图像合成个性化任务。通过引入锚定方向的概念，将查询过程转化为半开放问题，并提出了一种基于方向的不确定性采样策略来实现生成式主动学习并解决开发 - 探索的问题。通过大量实验验证了我们方法的有效性，表明开源模型在性能上可以比大公司开发的封闭模型（如 Google 的 StyleDrop）取得更好的结果。