Image Classification with Deep Reinforcement Active Learning

作     者：Jiu, Mingyuan Song, Xuguang Sahbi, Hichem Li, Shupan Chen, Yan Guo, Wei Guo, Lihua Xu, Mingliang 

作者机构：School of Computer and Artificial Intelligence Zhengzhou University China Engineering Research Center of Intelligent Swarm Systems Ministry of Education China National Supercomputing Center Zhengzhou China Sorbonne University CNRS LIP6 ParisF-75005 France Luoyang float glass group Co.Ltd State Key Laboratory of Advanced Technology for Float Glass China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Deep reinforcement learning 

摘      要：Deep learning is currently reaching outstanding performances on different tasks, including image classification, especially when using large neural networks. The success of these models is tributary to the availability of large collections of labeled training data. In many real-world scenarios, labeled data are scarce, and their hand-labeling is time, effort and cost demanding. Active learning is an alternative paradigm that mitigates the effort in hand-labeling data, where only a small fraction is iteratively selected from a large pool of unlabeled data, and annotated by an expert (a.k.a oracle), and eventually used to update the learning models. However, existing active learning solutions are dependent on handcrafted strategies that may fail in highly variable learning environments (datasets, scenarios, etc). In this work, we devise an adaptive active learning method based on Markov Decision Process (MDP). Our framework leverages deep reinforcement learning and active learning together with a Deep Deterministic Policy Gradient (DDPG) in order to dynamically adapt sample selection strategies to the oracle’s feedback and the learning environment. Extensive experiments conducted on three different image classification benchmarks show superior performances against several existing active learning strategies. Copyright © 2024, The Authors. All rights reserved.