Variational Autoencoders (VAE) are probabilistic deep generative models underpinned by elegant theory, stable training processes, and meaningful manifold representations. However, they produce blurry images due to a lack of explicit emphasis over high-frequency textural details of the images, and the difficulty to directly model the complex joint probability distribution over the high-dimensional image space. In this work, we approach these two challenges with a novel wavelet space VAE that uses the decoder to model the images in the wavelet coefficient space. This enables the VAE to emphasize over high-frequency components within an image obtained via wavelet decomposition. Additionally, by decomposing the complex function of generating high-dimensional images into inverse wavelet transformation and generation of wavelet coefficients, the latter becomes simpler to model by the VAE. We empirically validate that deep generative models operating in the wavelet space can generate images of higher quality than the image (RGB) space counterparts. Quantitatively, on benchmark natural image datasets, we achieve consistently better FID scores than VAE based architectures and competitive FID scores with a variety of GAN models for the same architectural and experimental setup. Furthermore, the proposed wavelet-based generative model retains desirable attributes like disentangled and informative latent representation without losing the quality in the generated samples.
3D Wavelet Latent Diffusion Model for Whole-Body MR-to-CT Modality Translation
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Information Science and Technology, ShanghaiTech University（上海交通大学信息科学与技术学院）；Shanghai United Imaging Healthcare Co., Ltd.（上海联影医疗科技有限公司）；Department of Radiology and Biomedical Imaging, Yale University（耶鲁大学放射科与生物医学成像系）；Department of Applied Mathematics & Statistics and Department of Computer Science, Stony Brook University（石溪大学应用数学与统计系和计算机科学系）
In recent years, considerable progress has been made in the visual quality of Generative Adversarial Networks (GANs). Even so, these networks still suffer from degradation in quality for high-frequency content, stemming from a spectrally biased architecture, and similarly unfavorable loss functions. To address this issue, we present a novel general-purpose Style and WAvelet based GAN (SWAGAN) that implements progressive generation in the frequency domain. SWAGAN incorporates wavelets throughout its generator and discriminator architectures, enforcing a frequency-aware latent representation at …
Wavelet-Fusion Diffusion Model for Multimodal Brain MRI Synthesis with Modality and Metadata Conditioning
Department of Psychology & Neuroscience, Auckland University of Technology（心理学与神经科学系，奥克兰技术大学）；Department of Psychiatry, University of Melbourne（精神病学系，墨尔本大学）
Institute of Image Communication and Network Engineering, Shanghai JiaoTong University（上海交通大学图像通信与网络工程研究所）
Traditional adversarial examples are typically generated by adding perturbation noise to the input image within a small matrix norm. In practice, un-restricted adversarial attack has raised great concern and presented a new threat to the AI safety. In this paper, we propose a wavelet-VAE structure to reconstruct an input image and generate adversarial examples by modifying the latent code. Different from perturbation-based attack, the modifications of the proposed method are not limited but imperceptible to human eyes. Experiments show that our method can generate high quality adversarial exam…
Wavelet Flow Matching for Multi-Scale Physics Emulation
Department of Earth and Environmental Engineering（地球与环境工程系）；Columbia University（哥伦比亚大学）；University of Edinburgh（爱丁堡大学）；Courant Institute of Mathematical Sciences（数学科学学院）；New York University（纽约大学）；Scripps Institution of Oceanography（斯克里普斯海洋研究所）；Halıcıoğlu Data Science Institute（哈利奇数据科学研究所）；University of California San Diego（加州大学圣地亚哥分校）；CUNY New York City College of Technology（纽约市立大学纽约技术学院）；Lamont-Doherty Earth Observatory（拉蒙特-多伊蒂地球观测站）
Protein Counterfactuals via Diffusion-Guided Latent Optimization
Machine Learning Group, Technische Universität Berlin（技术大学柏林机器学习组）；Berlin Institute for the Foundations of Learning and Data (BIFOLD)（柏林学习与数据基础研究所（BIFOLD））；BASF Digital Solutions GmbH（巴斯夫数字解决方案有限公司）
MotionWavelet: Human Motion Prediction via Wavelet Manifold Learning
Imperial College London（帝国理工学院）；The University of Hong Kong（香港大学）；University of Pennsylvania（宾夕法尼亚大学）；Hong Kong University of Science and Technology（香港科技大学）；Nanyang Technological University（南洋理工大学）；Shanghai AI Lab（上海人工智能实验室）；University of Cambridge（剑桥大学）；Texas A&M University（德州农工大学）
Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models
State Key Laboratory of Complex and Critical Software Environment（复杂与关键软件环境国家重点实验室）；Beihang University（北京航空航天大学）；School of Computer Science and Engineering（计算机科学与工程学院）
MSF: Efficient Diffusion Model Via Multi-Scale Latent Factorize
Huawei Inc.（华为公司）；Huazhong University of Science & Technology（华中科技大学）；The Chinese University of Hong Kong（香港中文大学）；School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）
LVMark: Robust Watermark for Latent Video Diffusion Models
Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea（人工智能系，韩国大学，首尔02841，大韩民国）；Google DeepMind, Mountain View, CA 94043, USA（谷歌DeepMind，山景城，加利福尼亚州94043，美国）
Institute of Physics, Chinese Academy of Sciences（中国科学院物理研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Songshan Lake Materials Laboratory（松山湖材料实验室）
We present a variational renormalization group (RG) approach using a deep generative model based on normalizing flows. The model performs hierarchical change-of-variables transformations from the physical space to a latent space with reduced mutual information. Conversely, the neural net directly maps independent Gaussian noises to physical configurations following the inverse RG flow. The model has an exact and tractable likelihood, which allows unbiased training and direct access to the renormalized energy function of the latent variables. To train the model, we employ probability density di…
FDS: Frequency-Aware Denoising Score for Text-Guided Latent Diffusion Image Editing
School of Computer and Communication Sciences, EPFL（EPFL计算机与通信科学学院）；Department of Electrical and Photonics Engineering, DTU（丹麦技术大学电气与光子工程系）；Department of Electrical Engineering, Chalmers University of Technology（查尔姆斯理工大学电气工程系）
National University of Singapore（新加坡国立大学）；ByteDance Inc.（字节跳动公司）
Despite the recent visually-pleasing results achieved, the massive computational cost has been a long-standing flaw for diffusion probabilistic models (DPMs), which, in turn, greatly limits their applications on resource-limited platforms. Prior methods towards efficient DPM, however, have largely focused on accelerating the testing yet overlooked their huge complexity and sizes. In this paper, we make a dedicated attempt to lighten DPM while striving to preserve its favourable performance. We start by training a small-sized latent diffusion model (LDM) from scratch, but observe a significant …
Wavelet-based Mamba with Fourier Adjustment for Low-light Image Enhancement
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology（上海理工大学光电信息与计算机工程学院）；School of computer science and artificial intelligence, Liaoning Normal University（辽宁师范大学计算机与人工智能学院）；College of computer science and technology, Jilin University（吉林大学计算机科学与技术学院）；School of Computer, National University of Dense Technology（国防科技大学计算机学院）
Wide accessibility of imaging and profile sensors in modern industrial systems created an abundance of high-dimensional sensing variables. This led to a a growing interest in the research of high-dimensional process monitoring. However, most of the approaches in the literature assume the in-control population to lie on a linear manifold with a given basis (i.e., spline, wavelet, kernel, etc) or an unknown basis (i.e., principal component analysis and its variants), which cannot be used to efficiently model profiles with a nonlinear manifold which is common in many real-life cases. We propose d…
SWAN: Self-supervised Wavelet Neural Network for Hyperspectral Image Unmixing
Info Edge India Ltd；Department of Artificial Intelligence and Machine Learning（人工智能与机器学习系）；Acharya Institute of Technology（阿查里亚理工学院）；Indian Institute of Information Technology Vadodara (IIITV)（瓦多拉印度信息科技学院）
TF-CoDiT: Conditional Time Series Synthesis with Diffusion Transformers for Treasury Futures
School of Software and Microelectronics, Peking University（软件与微电子学院，北京大学）；Financial Markets Division, Pingan Bank Co. Ltd（平安银行金融市场部）；China Electronics Cloud Technology Co., Ltd（中国电子云技术有限公司）
Qualcomm AI Research, Qualcomm Technologies, Inc.（高通技术公司高通人工智能研究院）；University of California Irvine（加州大学欧文分校）；Qualcomm AI Research, Qualcomm Technologies Netherlands B.V.（高通技术荷兰有限公司高通人工智能研究院）
We present PLONQ, a progressive neural image compression scheme which pushes the boundary of variable bitrate compression by allowing quality scalable coding with a single bitstream. In contrast to existing learned variable bitrate solutions which produce separate bitstreams for each quality, it enables easier rate-control and requires less storage. Leveraging the latent scaling based variable bitrate solution, we introduce nested quantization, a method that defines multiple quantization levels with nested quantization grids, and progressively refines all latents from the coarsest to the fines…
Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting
University Hospital Basel（巴塞尔大学医院）；Helmholtz Center Munich（慕尼黑亥姆霍兹中心）；Helmholtz AI（亥姆霍兹人工智能）；Technical University of Munich（慕尼黑工业大学）；University of Basel（巴塞尔大学）；University of Zurich（苏黎世大学）；Klinikum rechts der Isar（伊斯河畔医院）；TranslaTUM - Central Institute for Translational Cancer Research（慕尼黑工业大学转化肿瘤学中央研究所（TranslaTUM））
Ultra-High-Resolution Image Synthesis: Data, Method and Evaluation
State Key Laboratory of Complex and Critical Software Environment（复杂与关键软件环境国家重点实验室）；School of Computer Science and Engineering（计算机科学与工程学院）；Beihang University（北京航空航天大学）
Wavelet-based Decoupling Framework for low-light Stereo Image Enhancement
Shaanxi Key Laboratory for Network Computing and Security Technology（陕西网络计算与安全技术重点实验室）；School of Computer Science and Engineering（计算机科学与工程学院）；Xi'an University of Technology（西安理工大学）
