Order-free Learning Alleviating Exposure Bias in Multi-label Classification
This addresses a key limitation in multi-label classification for applications requiring robust generalization, though it is incremental as it builds on existing sequence prediction methods.
The paper tackles the problem of exposure bias in multi-label classification by proposing a framework that eliminates the need for predefined label orders in RNN decoders, resulting in outperforming baselines by a large margin on benchmark datasets and generating more unseen label combinations.
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.