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Deep neural networks have been adopted in multi-label classification for their excellent performance, however, existing methods fail to comprehensively utilize the high-order correlations between instances and the high-order correlations between labels, and these methods are difficult to deal with label noise effectively. We propose a novel end-to-end deep framework named Robust Fused Hypergraph Neural Networks for Multi-Label Classification (RFHNN), which can effectively utilize the two kinds of high-order correlations and adopt them to mitigate the impact of label noise. In RFHNN, Hypergraph...

Noise in label space is a major challenge in the multi-label classification problem, as the noise (including false-negative noise and false-positive noise) can affect the distribution of the label space, which causes serious interference to the performance of the learned model. Existing methods have initially solved the situation where false-negative noise and false-positive noise appear separately, but classification when the two kinds of noise appear at the same time is still a challenging problem that has not yet been solved. This paper proposed a novel method named Dual-Scale Correlation A...