Deep Transfer Learning for Image Emotion Analysis: Reducing Marginal and Joint Distribution Discrepancies Together

Abstract

A lot of research attentions have been paid to image emotion analysis in recent years. Meanwhile, as convolutional neural networks (CNNs) have made great successful in computer vision, many researchers start to employ CNN to discriminate image emotions. However, the training procedure of CNNs depends on sufficient labeled data. Therefore, a CNN is hard to perform well in an image domain with scant labeled information. In this paper, we propose a deep transfer learning method for image emotion analysis. The method can leverage rich emotion knowledge from a source domain to the target domain. Our method reduces both marginal and joint domain distribution discrepancies at fully-connected layers. Through this way, we can effectively extract more transferable features and advance the performance of CNNs on poor-label emotion-image domains.