Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network
arxiv(2023)
摘要
Model Parsing defines the research task of predicting hyperparameters of the
generative model (GM), given a generated image as input. Since a diverse set of
hyperparameters is jointly employed by the generative model, and dependencies
often exist among them, it is crucial to learn these hyperparameter
dependencies for the improved model parsing performance. To explore such
important dependencies, we propose a novel model parsing method called
Learnable Graph Pooling Network (LGPN). Specifically, we transform model
parsing into a graph node classification task, using graph nodes and edges to
represent hyperparameters and their dependencies, respectively. Furthermore,
LGPN incorporates a learnable pooling-unpooling mechanism tailored to model
parsing, which adaptively learns hyperparameter dependencies of GMs used to
generate the input image. We also extend our proposed method to CNN-generated
image detection and coordinate attacks detection. Empirically, we achieve
state-of-the-art results in model parsing and its extended applications,
showing the effectiveness of our method. Our source code are available.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要