Mutual match for semi-supervised online evolutive learning
Di Li, Xiaoguang Zhu, Liang Song
Abstract
Abstract Semi-supervised learning (SSL) can utilize a large amount of unlabeled data for self-training and continuous evolution with only a few annotations. This feature makes SSL a potential candidate for dealing with data from changing and real-time environments, where deep-learning models need to be adapting to evolving and nonstable (non-i.i.d.) data streams from the real world, i.e., online evolutive scenarios. However, state-of-the-art SSL methods often have complex model design mechanisms and may cause performance degradation in a generalized and open environment. In an edge computing setup, e.g., typical in modern Internet of Things (IoT) applications, a multi-agent SSL architecture can help resolve generalization problems by sharing knowledge between models. In this paper, we introduce Mutual Match (MM), an online-evolutive SSL algorithm that integrates mutual interactive learning and soft-supervision consistency regularization, as well as unsupervised sample mining. By leveraging extra knowledge in the training process and the interactive collaboration between models, MM surpasses multiple top SSL algorithms in accuracy and convergence efficiency under the same online-evolutive experiment setup. MM simplifies the complexity of model design and follows a unified and easy-to-expandable pipeline, which can be beneficial to tasks with insufficient labeled data and frequently changing data distribution.