Multi-Task Learning of Classification and Generation for Set-Structured Data

概要

In this study we propose a multi-task learning model of classification and generation for set-structured data. The proposed model learns data generation and classification in a single neural network by integrating a classification layer into a variational autoencoder while maintaining permutation invariance and equivariance nature which are characteristics of set-structured data. The proposed model allows for semi-supervised learning in set-structured data classification and can also be applied to confidence calibration using the input data distribution estimated by the generative model. In the experiments we evaluated the performance of the proposed model in a semi-supervised classification task on set-structured datasets and compared it with a baseline model consisting only of a classifier. The results demonstrated that simultaneous learning of the classification and generation effectively improves the classification accuracy and confidence reliability for set-structured data even with a limited number of labeled data.

佐藤 史興

博士前期課程学生

早志英朗

准教授

深層学習やベイズ推定を基盤とした機械学習アルゴリズムの開発を中心に、生体信号解析、医用画像処理などの応用研究に従事。

長原一

教授

コンピューテーショナルフォトグラフィ、コンピュータビジョンを専門とし実世界センシングや情報処理技術、画像認識技術の研究を行う。さらに、画像センシングにとどまらず様々なセンサに拡張したコンピュテーショナルセンシング手法の開発や高次元で冗長な実世界ビッグデータから意味のある情報を計測するスパースセンシングへの転換を目指す。