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
Scalable Label Distribution Learning (SLDL) is proposed for multi-label classification, where different labels are described as distributions in a latent space with asymmetric correlation, independent of the number of labels, resulting in little computational consumption.