SEAN: Image Synthesis with Semantic Region-Adaptive Normalization

We propose semantic region-adaptive normalization (SEAN), a simple but effective building block for Generative Adversarial Networks conditioned on segmentation masks that describe the semantic regions in the desired output image. Using SEAN normalization, we can build a network architecture that can control the style of each semantic region individually, e.g., we can specify one style reference image per region. SEAN is better suited to encode, transfer, and synthesize style than the best previous method in terms of reconstruction quality, variability, and visual quality. We evaluate SEAN on multiple datasets and report better quantitative metrics (e.g. FID, PSNR) than the current state of the art. SEAN also pushes the frontier of interactive image editing. We can interactively edit images by changing segmentation masks or the style for any given region. We can also interpolate styles from two reference images per region.Comment: Accepted as a CVPR 2020 oral paper. The interactive demo is available at https://youtu.be/0Vbj9xFgoU

Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?

We propose an efficient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHQ dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful.Comment: Accepted for oral presentation at ICCV 2019, "For videos visit https://youtu.be/RnTXLXw9o_I , https://youtu.be/zJoYY2eHAF0 and https://youtu.be/bA893L-PjbI

How does Lipschitz regularization influence GAN training?

Despite the success of Lipschitz regularization in stabilizingGAN training, the exact reason of its effectiveness remains poorly un-derstood. The direct effect ofK-Lipschitz regularization is to restrict theL2-norm of the neural network gradient to be smaller than a thresholdK(e.g.,K= 1) such that‖∇f‖≤K. In this work, we uncover an evenmore important effect of Lipschitz regularization by examining its im-pact on the loss function:It degenerates GAN loss functions to almostlinear ones by restricting their domain and interval of attainable gradi-ent values. Our analysis shows that loss functions are only successful ifthey are degenerated to almost linear ones. We also show that loss func-tions perform poorly if they are not degenerated and that a wide rangeof functions can be used as loss function as long as they are sufficientlydegenerated by regularization. Basically, Lipschitz regularization ensuresthat all loss functionseffectively work in the same way.Empirically, weverify our proposition on the MNIST, CIFAR10 and CelebA datasets

Fast and exact geodesic computation using Edge-based Windows Grouping.

Computing discrete geodesic distance over triangle meshes is one of the fundamental problems in computational geometry and computer graphics. As the “Big Data Era” arrives, a fast and accurate solution to the geodesic computation problem on large scale models with constantly increasing resolutions is desired. However, it is still challenging to deal with the speed, memory cost and accuracy of the geodesic computation at the same time. This thesis addresses the aforementioned challenge by proposing the Edge- based Windows Grouping (EWG) technique. With the local geodesic information encoded in a “window”, EWG groups the windows based on the mesh edges and processes them together. Thus, the interrelationships among the grouped windows can be utilized to improve the performance of geodesic computation on triangle meshes. Based on EWG, a novel exact geodesic algorithm is proposed in this thesis, which is fast, accurate and memory-efficient. This algorithm computes the geodesic distances at mesh vertices by propagating the geodesic information from the source over the entire mesh. Its high performance comes from its low computational redundancy and management overhead, which are both introduced by EWG. First, the redundant windows on an edge can be removed by comparing its distance with those of the other windows on the same edge. Second, the windows grouped on an edge usually have similar geodesic distances and can be propagated in batches efficiently. To the best of my knowledge, the proposed exact geodesic algorithm is the fastest and most memory-efficient one among all existing methods. In addition, the proposed exact geodesic algorithm is revised and employed to construct the geodesic-metric-based Voronoi diagram on triangle meshes. In this application, the geodesic computation is the bottleneck in both the time and memory costs. The proposed method achieves low memory cost from the key observation that the Voronoi diagram boundaries usually only cross a minority of the meshes’ triangles and most of the windows stored on edges are redundant. As a result, the proposed method resolves the memory bottleneck of the Voronoi diagram construction without sacrificing its speed

PypeR, A Python Package for Using R in Python

This article describes PypeR, a Python package which allows the R language to be called in Python using the pipe communication method. By running R through pipe, the Python program gains flexibility in sub-process controls, memory control, and portability across popular operating system platforms, including Windows, GNU Linux and Mac OS X. PypeR can be downloaded at http://rinpy.sourceforge.net/

Hypoglycemic Properties of Oxovanadium (IV) Coordination Compounds with Carboxymethyl-Carrageenan and Carboxymethyl-Chitosan in Alloxan-Induced Diabetic Mice

In order to avoid low absorption, incorporation, and undesirable side effects of inorganic oxovanadium compounds, the antidiabetic activities of organic oxovanadium (IV) compounds in alloxan-induced diabetic mice were investigated. Vanadyl carboxymethyl carrageenan (VOCCA) and vanadyl carboxymethyl chitosan (VOCCH) were synthesized and administrated through intragastric administration in different doses for 20 days in alloxan-induced diabetic mice. Glibenclamide was administrated as the positive control. Our results showed that low-dose group, middle-dose group, and high-dose group of VOCCA and VOCCH could significantly reduce the levels of blood glucose (P < 0.05) compared with untreated group, but not in normal mice. Besides, high-dose groups of VOCCA and VOCCH exhibited more significant hypoglycemic activities (P < 0.01). After treated with VOCCH, the oral glucose tolerance of high-dose group of VOCCH was improved compared with model control group (P < 0.05)