A Survey on the New Generation of Deep Learning in Image Processing <s> 4) PTGAN <s> Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain $X$ to a target domain $Y$ in the absence of paired examples. Our goal is to learn a mapping $G: X \rightarrow Y$ such that the distribution of images from $G(X)$ is indistinguishable from the distribution $Y$ using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping $F: Y \rightarrow X$ and introduce a cycle consistency loss to push $F(G(X)) \approx X$ (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc. Quantitative comparisons against several prior methods demonstrate the superiority of our approach. <s> BIB001 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 4) PTGAN <s> Although the performance of person Re-Identification (ReID) has been significantly boosted, many challenging issues in real scenarios have not been fully investigated, e.g., the complex scenes and lighting variations, viewpoint and pose changes, and the large number of identities in a camera network. To facilitate the research towards conquering those issues, this paper contributes a new dataset called MSMT17 with many important features, e.g., 1) the raw videos are taken by an 15-camera network deployed in both indoor and outdoor scenes, 2) the videos cover a long period of time and present complex lighting variations, and 3) it contains currently the largest number of annotated identities, i.e., 4,101 identities and 126,441 bounding boxes. We also observe that, domain gap commonly exists between datasets, which essentially causes severe performance drop when training and testing on different datasets. This results in that available training data cannot be effectively leveraged for new testing domains. To relieve the expensive costs of annotating new training samples, we propose a Person Transfer Generative Adversarial Network (PTGAN) to bridge the domain gap. Comprehensive experiments show that the domain gap could be substantially narrowed-down by the PTGAN. <s> BIB002
The goal of PTGAN is to realize the migration of background style under the premise of keeping pedestrian foreground BIB002 . This person transfer procedure was inspired by the CycleGAN BIB001 (see in Fig.10 ). To keep the following description better understanding, we have markings, where x f and x b is the person foregrounds and backgrounds for x, respectively. and M (x) represents the foreground mask of x. Similarly, we have the same markings for y. How to capture person style transfer from x to y ? that is, we can obtain new image x f + y b or x b + y f . Here, the symbol ' ' denotes hadamard product. Firstly, for CycleGAN, the objective function of style transfer learning can be formulated as follows: where L cycle (G x , G y ) denotes the cycle consistency loss. and we have the following formulation, Secondly, to avoid the appearance of pedestrians in style transfer may change, the objective function of identity (person or foreground) loss can be formulated as follows: Different transferred samples of one person regard as having the same person. Based on the above discussions, PTGAN can be constructed to satisfy two constraints, i.e., the style transfer and person identity keeping. Therefore, We thus formulate the loss function of PTGAN as follows, where γ is the parameter for the trade-off between two losses. In practice, Extensive experiments show that PTGAN effectively reduces the domain gap.