[1O4-GS-7] Vision, speech media processing

[1O4-GS-7-01] Concept Composition by Energy-Based Model using Order Embedding

〇Kota Sueyoshi¹, Takashi Matsubara¹ (1. Osaka University)

Keywords:deep learning, energy based model

Energy-based model (EBM) is a deep generative model that learns data distribution by computing energy functions using neural networks. For concept composition using EBM, it is known that multiple concepts can be constructed by summing energy functions under the assumption of concept independence. However, assuming independence of concepts may lead to a lack of diversity in the generated data. Therefore, we propose a method to embed concept information into the latent space by order embedding. By performing a max operation among the coordinates of the embedded concepts, we can expect to generate images with the specified combination of concepts. Experimental results show that the proposed method can generate images with multiple concepts without assuming the independence of concepts.