条件付き変分オートエンコーダにおけるマルチモーダル潜在空間の証拠的スパース化
Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders
学習済み条件付きVAEの離散潜在空間から、入力条件に直接証拠を持つ潜在クラスだけを証拠理論で選別し、マルチモーダル性を保ちつつサンプル空間を縮小する事後手法を提案した。
著者: Masha Itkina, Boris Ivanovic, Ransalu Senanayake, Mykel J. Kochenderfer, Marco Pavone
分類: cs.LG, cs.AI, cs.CV, cs.RO
原文アブストラクト
Discrete latent spaces in variational autoencoders have been shown to effectively capture the data distribution for many real-world problems such as natural language understanding, human intent prediction, and visual scene representation. However, discrete latent spaces need to be sufficiently large to capture the complexities of real-world data, rendering downstream tasks computationally challenging. For instance, performing motion planning in a high-dimensional latent representation of the environment could be intractable. We consider the problem of sparsifying the discrete latent space of a trained conditional variational autoencoder, while preserving its learned multimodality. As a post hoc latent space reduction technique, we use evidential theory to identify the latent classes that receive direct evidence from a particular input condition and filter out those that do not. Experiments on diverse tasks, such as image generation and human behavior prediction, demonstrate the effectiveness of our proposed technique at reducing the discrete latent sample space size of a model while maintaining its learned multimodality.
