EmotionGAN

Deep neural networks have performed well on various benchmark vision tasks with large-scale labeled training data; however, such training data is expensive and time-consuming to obtain. Due to domain shift or dataset bias, directly transferring models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain often results in poor performance. In this paper, we consider the domain adaptation problem in image emotion recognition. Specifically, we study how to adapt the discrete probability distributions of image emotions from a source domain to a target domain in an unsupervised manner. We develop a novel adversarial model for emotion distribution learning, termed EmotionGAN, which alternately optimizes the Generative Adversarial Network (GAN) loss, semantic consistency loss, and regression loss. The EmotionGAN model can adapt source domain images such that they appear as if they were drawn from the target domain, while preserving the annotation information. Extensive experiments are conducted on the FlickrLDL and TwitterLDL datasets, and the results demonstrate the superiority of the proposed method as compared to state-of-the-art approaches.