A Survey on the New Generation of Deep Learning in Image Processing <s> 6) DESCRIPTION OF OTHER MODELS IN GAN SERIES <s> In this work, we present the Text Conditioned Auxiliary Classifier Generative Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network (GAN) for synthesizing images from their text descriptions. Former approaches have tried to condition the generative process on the textual data; but allying it to the usage of class information, known to diversify the generated samples and improve their structural coherence, has not been explored. We trained the presented TAC-GAN model on the Oxford-102 dataset of flowers, and evaluated the discriminability of the generated images with Inception-Score, as well as their diversity using the Multi-Scale Structural Similarity Index (MS-SSIM). Our approach outperforms the state-of-the-art models, i.e., its inception score is 3.45, corresponding to a relative increase of 7.8% compared to the recently introduced StackGan. A comparison of the mean MS-SSIM scores of the training and generated samples per class shows that our approach is able to generate highly diverse images with an average MS-SSIM of 0.14 over all generated classes. <s> BIB001 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 6) DESCRIPTION OF OTHER MODELS IN GAN SERIES <s> Inspired by classic Generative Adversarial Networks (GANs), we propose a novel end-to-end adversarial neural network, called SegAN, for the task of medical image segmentation. Since image segmentation requires dense, pixel-level labeling, the single scalar real/fake output of a classic GAN’s discriminator may be ineffective in producing stable and sufficient gradient feedback to the networks. Instead, we use a fully convolutional neural network as the segmentor to generate segmentation label maps, and propose a novel adversarial critic network with a multi-scale L1 loss function to force the critic and segmentor to learn both global and local features that capture long- and short-range spatial relationships between pixels. In our SegAN framework, the segmentor and critic networks are trained in an alternating fashion in a min-max game: The critic is trained by maximizing a multi-scale loss function, while the segmentor is trained with only gradients passed along by the critic, with the aim to minimize the multi-scale loss function. We show that such a SegAN framework is more effective and stable for the segmentation task, and it leads to better performance than the state-of-the-art U-net segmentation method. We tested our SegAN method using datasets from the MICCAI BRATS brain tumor segmentation challenge. Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision. <s> BIB002 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 6) DESCRIPTION OF OTHER MODELS IN GAN SERIES <s> Coupled Generative Adversarial Network (CoGAN) was recently introduced in order to model a joint distribution of a multi modal dataset. The CoGAN model lacks the capability to handle noisy data as well as it is computationally expensive and inefficient for practical applications such as cross-domain image transformation. In this paper, we propose a new method, named the Coupled Generative Adversarial Stacked Auto-encoder (CoGASA), to directly transfer data from one domain to another domain with robustness to noise in the input data as well to as reduce the computation time. We evaluate the proposed model using MNIST and the Large-scale CelebFaces Attributes (CelebA) datasets, and the results demonstrate a highly competitive performance. Our proposed models can easily transfer images into the target domain with minimal effort. <s> BIB003
TAC-GAN is a text-to-image GAN framework for synthesizing images from their text descriptions BIB001 . TAC-GAN builds upon the conditional auxiliary classifier GANs by conditioning the generated images on a text description instead of on a class label. For the presented TAC-GAN model, the input vector of the Generative network is built based on a noise vector and another vector containing an embedded representation of the textual description. While the Discriminator is similar to that of the TAC-GAN, it is also augmented to receive the text information as input before performing its classification. SegAN mainly designs for the task of medical image segmentation, which includes two parts: segmentor network and critic network BIB002 . Unlike classical GAN architecture, they use an FCN as the segmentor to generate segmentation label maps and propose a novel adversarial critic network with a multi-scale L 1 loss function to force the critic and segmentor to learn both global and local features that capture long-and short-range spatial relationships between pixels. Finally, the segmentor and critic networks can be trained in an alternating fashion in a min-max game. CoGAN is an improvement framework based on the GAN framework, which has established as a viable solution to image distribution learning tasks BIB003 . Moreover, CoGAN can extend GAN for joint image distribution learning tasks. Precisely, CoGAN consists of a tuple of GAN, each for one image domain. The CoGAN learns a product of marginal distributions rather than a joint distribution.