We present a novel variational generative adversarial network (VGAN) based on Wasserstein loss to learn a latent representation from a face image that is invariant to identity but preserves head-pose information. This facilitates synthesis of a realistic face image with the same head pose as a given input image, but with a different identity. One application of this network is in privacy-sensitive scenarios; after identity replacement in an image, utility, such as head pose, can still be recovered. Extensive experimental validation on synthetic and real human-face image datasets performed under 3 threat scenarios confirms the ability of the proposed network to preserve head pose of the input image, mask the input identity, and synthesize a good-quality realistic face image of a desired identity. We also show that our network can be used to perform pose-preserving identity morphing and identity-preserving pose morphing. The proposed method improves over a recent state-of-the-art method in terms of quantitative metrics as well as synthesized image quality.
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
We propose a new class of physics-informed neural networks, called physics-informed Variational Autoencoder (PI-VAE), to solve stochastic differential equations (SDEs) or inverse problems involving SDEs. In these problems the governing equations are known but only a limited number of measurements of system parameters are available. PI-VAE consists of a variational autoencoder (VAE), which generates samples of system variables and parameters. This generative model is integrated with the governing equations. In this integration, the derivatives of VAE outputs are readily calculated using automat…
Dalian University of Technology（大连理工大学）；University of Central Florida（中佛罗里达大学）
Here, we explore the low-level statistics of images generated by state-of-the-art deep generative models. First, Variational auto-encoder (VAE~\cite{kingma2013auto}), Wasserstein generative adversarial network (WGAN~\cite{arjovsky2017wasserstein}) and deep convolutional generative adversarial network (DCGAN~\cite{radford2015unsupervised}) are trained on the ImageNet dataset and a large set of cartoon frames from animations. Then, for images generated by these models as well as natural scenes and cartoons, statistics including mean power spectrum, the number of connected components in a given i…
University of Central Florida（中佛罗里达大学）；Beijing Jiaotong University（北京交通大学）；Tencent AI Lab（腾讯人工智能实验室）
Despite being impactful on a variety of problems and applications, the generative adversarial nets (GANs) are remarkably difficult to train. This issue is formally analyzed by \cite{arjovsky2017towards}, who also propose an alternative direction to avoid the caveats in the minmax two-player training of GANs. The corresponding algorithm, called Wasserstein GAN (WGAN), hinges on the 1-Lipschitz continuity of the discriminator. In this paper, we propose a novel approach to enforcing the Lipschitz continuity in the training procedure of WGANs. Our approach seamlessly connects WGAN with one of the …
School of Computer Engineering, KIIT Deemed to be University（KIIT 大学计算机工程学院）
While generative adversarial networks (GAN) are popular for their higher sample quality as opposed to other generative models like the variational autoencoders (VAE) and Boltzmann machines, they suffer from the same difficulty of the evaluation of generated samples. Various aspects must be kept in mind, such as the quality of generated samples, the diversity of classes (within a class and among classes), the use of disentangled latent spaces, agreement of said evaluation metric with human perception, etc. In this paper, we propose a new score, namely, GM Score, which takes into various factors…
Stevens Institute of Technology（史蒂文斯理工学院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
This paper proposes a joint training method to learn both the variational auto-encoder (VAE) and the latent energy-based model (EBM). The joint training of VAE and latent EBM are based on an objective function that consists of three Kullback-Leibler divergences between three joint distributions on the latent vector and the image, and the objective function is of an elegant symmetric and anti-symmetric form of divergence triangle that seamlessly integrates variational and adversarial learning. In this joint training scheme, the latent EBM serves as a critic of the generator model, while the gen…
Why Do DiT Editors Drift? Plug-and-Play Low Frequency Alignment in VAE Latent Space
Tsinghua University（清华大学）；Carnegie Mellon University（卡内基梅隆大学）；Peking University（北京大学）；CASIA；University of Electronic Science and Technology of China（电子科技大学）；Guangming Laboratory（光明实验室）
Dynamic User Interface Generation for Enhanced Human-Computer Interaction Using Variational Autoencoders
University of Southern California（南加州大学）；School of Visual Arts（视觉艺术学院）；Georgia Institute of Technology（佐治亚理工学院）；Carnegie Mellon University（卡内基梅隆大学）；New York University（纽约大学）
We present a new flavor of Variational Autoencoder (VAE) that interpolates seamlessly between unsupervised, semi-supervised and fully supervised learning domains. We show that unlabeled datapoints not only boost unsupervised tasks, but also the classification performance. Vice versa, every label not only improves classification, but also unsupervised tasks. The proposed architecture is simple: A classification layer is connected to the topmost encoder layer, and then combined with the resampled latent layer for the decoder. The usual evidence lower bound (ELBO) loss is supplemented with a supe…
Korea Advanced Institute of Science and Technology（韩国科学技术院）
As deep learning is showing unprecedented success in medical image analysis tasks, the lack of sufficient medical data is emerging as a critical problem. While recent attempts to solve the limited data problem using Generative Adversarial Networks (GAN) have been successful in generating realistic images with diversity, most of them are based on image-to-image translation and thus require extensive datasets from different domains. Here, we propose a novel model that can successfully generate 3D brain MRI data from random vectors by learning the data distribution. Our 3D GAN model solves both i…
Generative models are known to be difficult to assess. Recent works, especially on generative adversarial networks (GANs), produce good visual samples of varied categories of images. However, the validation of their quality is still difficult to define and there is no existing agreement on the best evaluation process. This paper aims at making a step toward an objective evaluation process for generative models. It presents a new method to assess a trained generative model by evaluating the test accuracy of a classifier trained with generated data. The test set is composed of real images. There…
AtomAI is an open-source software package bridging instrument-specific Python libraries, deep learning, and simulation tools into a single ecosystem. AtomAI allows direct applications of the deep convolutional neural networks for atomic and mesoscopic image segmentation converting image and spectroscopy data into class-based local descriptors for downstream tasks such as statistical and graph analysis. For atomically-resolved imaging data, the output is types and positions of atomic species, with an option for subsequent refinement. AtomAI further allows the implementation of a broad range of …
DCTdiff: Intriguing Properties of Image Generative Modeling in the DCT Space
Utrecht University（乌得勒支大学）；Moonshot AI（月之暗面）；Shandong University（山东大学）；Max Planck Institute for Psycholinguistics（马克斯·普朗克心理语言学研究所）；University of Innsbruck（因斯布鲁克大学）
ResetEdit: Precise Text-guided Editing of Generated Image via Resettable Starting Latent
Shanghai Jiao Tong University（上海交通大学）；University of Science and Technology of China（科学技术大学）；Wuhan University（武汉大学）；National University of Singapore（新加坡国立大学）
MeshCraft: Exploring Efficient and Controllable Mesh Generation with Flow-based DiTs
Tsinghua University（清华大学）；Shanghai Jiaotong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；The University of Sydney（悉尼大学）；VAST（VAST机构）；The Chinese University of Hong Kong（香港中文大学）
RelightMaster: Precise Video Relighting with Multi-plane Light Images
Multimedia Laboratory, The Chinese University of Hong Kong（香港中文大学多媒体实验室）；Kling Team, Kuaishou Technology（快手技术 Kling 团队）；CPII under InnoHK（创新香港 CPII）；Zhejiang University（浙江大学）；Dalian University of Technology（大连理工大学）
