State-Relabeling Adversarial Active Learning

• URL: http://arxiv.org/abs/2004.04943v1
• Date: Fri, 10 Apr 2020 08:23:59 GMT
• Title: State-Relabeling Adversarial Active Learning
• Authors: Beichen Zhang (1), Liang Li (2), Shijie Yang (1, 2), Shuhui Wang (2), Zheng-Jun Zha (3), Qingming Huang (1, 2, 4) ((1) University of Chinese Academy of Sciences. (2) Key Lab of Intell. Info. Process., Inst. of Comput. Tech., Chinese Academy of Sciences. (3) University of Science and Technology of China. (4) Peng Cheng Laboratory.)
• Abstract summary: Active learning is to design label-efficient algorithms by sampling the most representative samples to be labeled by an oracle. We propose a state relabeling adversarial active learning model (SRAAL), that leverages both the annotation and the labeled/unlabeled state information. Our model outperforms the previous state-of-art active learning methods and our initially sampling algorithm achieves better performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Active learning is to design label-efficient algorithms by sampling the most representative samples to be labeled by an oracle. In this paper, we propose a state relabeling adversarial active learning model (SRAAL), that leverages both the annotation and the labeled/unlabeled state information for deriving the most informative unlabeled samples. The SRAAL consists of a representation generator and a state discriminator. The generator uses the complementary annotation information with traditional reconstruction information to generate the unified representation of samples, which embeds the semantic into the whole data representation. Then, we design an online uncertainty indicator in the discriminator, which endues unlabeled samples with different importance. As a result, we can select the most informative samples based on the discriminator's predicted state. We also design an algorithm to initialize the labeled pool, which makes subsequent sampling more efficient. The experiments conducted on various datasets show that our model outperforms the previous state-of-art active learning methods and our initially sampling algorithm achieves better performance.

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