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In this paper, we focus on the facial expression translation task and propose a novel Expression Conditional GAN (EC-GAN) which can learn the mapping from one image domain to another one based on an additional expression attribute. The proposed ECGAN is a generic framework and is applicable to different expression generation tasks where specific facial expression can be easily controlled by the conditional attribute label. Besides, we introduce a novel face mask loss to reduce the influence of background changing. Moreover, we propose an entire framework for facial expression generation and recognition in the wild, which consists of two modules, i.e., generation and recognition. Finally, we evaluate our framework on several public face datasets in which the subjects have different races, illumination, occlusion, pose, color, content and background conditions. Even though these datasets are very diverse, both the qualitative and quantitative results demonstrate that our approach is able to generate facial expressions accurately and robustly.

An underlying assumption in bearing fault diagnosis is that the training data from a source domain and the test data from a target domain obey the same distribution. But this assumption can be easily violated in practical industrial environments due to domain shift, which leads to significant performance degradation. To overcome this issue, we propose a novel convolutional neural network model to identify cross-domain bearing fault types based on 1-D vibration signals. Different from current single-networkbased approaches, our model comprises a student network and a teacher network that simultaneously conduct data distribution matching and discriminative feature learning. Moreover, the two networks promote each other with the label prediction consistency constraint, so that the discriminative knowledge is able to transfer between the domains. Our model bridges the semantic information of the source vibration signals and the distribution information of the target vibration signals by jointly performing cross-domain feature disentanglement and adaptation. The proposed method is evaluated extensively on the Case Western Reserve University bearing fault dataset in two scenarios: varying working loads and different sensor locations. Experimental results show the superior performance of our method compared with existing shallow and deep learning methods in the literature. INDEX TERMS Bearing fault diagnosis, domain shift, convolutional neural networks, bearing reliability, mean-teacher model, rotating machinery, domain adaptation, knowledge transfer.

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