Order-free learning alleviating exposure bias in multi-label classification
Journal
AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
Pages
6038-6045
Date Issued
2020
Author(s)
Tsai C.-P
Abstract
Multi-label classification (MLC) assigns multiple labels to each sample. Prior studies show that MLC can be transformed to a sequence prediction problem with a recurrent neural network (RNN) decoder to model the label dependency. However, training a RNN decoder requires a predefined order of labels, which is not directly available in the MLC specification. Besides, RNN thus trained tends to overfit the label combinations in the training set and have difficulty generating unseen label sequences. In this paper, we propose a new framework for MLC which does not rely on a predefined label order and thus alleviates exposure bias. The experimental results on three multi-label classification benchmark datasets show that our method outperforms competitive baselines by a large margin. We also find the proposed approach has a higher probability of generating label combinations not seen during training than the baseline models. The result shows that the proposed approach has better generalization capability. Copyright ? 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Subjects
Decoding; Large dataset; Recurrent neural networks; Benchmark datasets; Generalization capability; Label combinations; Label dependencies; Multi label classification; Multiple labels; Recurrent neural network (RNN); Sequence prediction; Classification (of information)
Type
conference paper