Multi-Scale Label Relation Learning for Multi-Label Classification Using 1-Dimensional Convolutional Neural Networks

• URL: http://arxiv.org/abs/2107.05941v1
• Date: Tue, 13 Jul 2021 09:26:34 GMT
• Title: Multi-Scale Label Relation Learning for Multi-Label Classification Using 1-Dimensional Convolutional Neural Networks
• Authors: Junhyung Kim, Byungyoon Park, Charmgil Hong
• Abstract summary: We present Multi-Scale Label Dependence Relation Networks (MSDN), a novel approach to multi-label classification (MLC) MSDN uses 1-dimensional convolution kernels to learn label dependencies at multi-scale. We demonstrate that our model can achieve better accuracies with much smaller number of model parameters compared to RNN-based MLC models.
• Score: 0.5801044612920815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Multi-Scale Label Dependence Relation Networks (MSDN), a novel approach to multi-label classification (MLC) using 1-dimensional convolution kernels to learn label dependencies at multi-scale. Modern multi-label classifiers have been adopting recurrent neural networks (RNNs) as a memory structure to capture and exploit label dependency relations. The RNN-based MLC models however tend to introduce a very large number of parameters that may cause under-/over-fitting problems. The proposed method uses the 1-dimensional convolutional neural network (1D-CNN) to serve the same purpose in a more efficient manner. By training a model with multiple kernel sizes, the method is able to learn the dependency relations among labels at multiple scales, while it uses a drastically smaller number of parameters. With public benchmark datasets, we demonstrate that our model can achieve better accuracies with much smaller number of model parameters compared to RNN-based MLC models.

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