[3J3-OS-3a] AutoML（自動機械学習）(1/2)

[3J3-OS-3a-03] Exploring optimized semi-supervised learning using knowledge transfer graphs

〇Yoshitaka Muramoto¹, Tsubasa Hirakawa¹, Takayoshi Yamashita¹, Hironobu Fujiyoshi¹ (1. Chubu University)

[[Online]]

Keywords:Semi-Supervised Learning

Π-model is a consistency-based, semi-supervised learning (SSL) method that can be derived from other conventional methods by devising main components such as data augmentations and models. Also, FixMatch combines conventional data augmentation methods with pseudo-labeling to achieve higher accuracy. The structures of these SSL methods were designed by humans and may not be the best learning method. In this paper, we aim to explore a new SSL method that contains the conventional methods. We introduce consistency loss, pseudo-labeling, and other main components of conventional methods into the knowledge transfer graph that contains mutual learning, and explore the graph structure to obtain the new SSL method from various SSL methods. From the explore and evaluation experiments using various datasets such as CIFAR-100, we confirmed that our method is more accurate than the conventional SSL methods.