Recent studies have explored using pretrained Vision Foundation Models (VFMs) such as DINO for generative autoencoders, showing strong generative performance. Unfortunately, existing approaches often suffer from limited reconstruction fidelity due to the loss of high-frequency details. In this work, we present the \textbf{\em Hyperspherical Autoencoder (HAE)}, a framework that bridges semantic representation and pixel-level reconstruction. Our key insight is that while semantic information in contrastive representations is primarily directional, enforcing strict magnitude matching hinders the preservation of fine-grained details. To address this, we introduce a {\em Directional Feature Alignment} objective that enforces semantic consistency while allowing flexible feature magnitudes for detail retention, alongside a {\em Hierarchical Convolutional Patch Embedding} module to enhance local structure preservation. Furthermore, observing that SSL-based representations intrinsically lie on a hypersphere, we employ {\em Riemannian Flow Matching} to train a Diffusion Transformer (DiT) directly on this spherical latent manifold. Notably, our manifold-aware DiT exhibits highly efficient convergence, achieving an exceptional gFID of \textbf{1.96} alongside a reconstruction rFID of \textbf{0.78} and a PSNR of \textbf{25.2} dB, validating the advantages of our manifold-aware approach.
Eindhoven University of Technology（埃因霍温理工大学）；Prosus AI（Prosus人工智能）；Prosus
A disentangled representation of a data set should be capable of recovering the underlying factors that generated it. One question that arises is whether using Euclidean space for latent variable models can produce a disentangled representation when the underlying generating factors have a certain geometrical structure. Take for example the images of a car seen from different angles. The angle has a periodic structure but a 1-dimensional representation would fail to capture this topology. How can we address this problem? The submissions presented for the first stage of the NeurIPS2019 Disentan…
Learning Holographic Reduced Representations with Clifford Variational Autoencoders
Institute of Neuroinformatics（神经信息学研究所）；University of Zurich & ETH Zurich（苏黎世大学与苏黎世联邦理工学院）；National Research Council Canada（加拿大国家研究委员会）；University of Waterloo（滑铁卢大学）
We present fast, realistic image generation on high-resolution, multimodal datasets using hierarchical variational autoencoders (VAEs) trained on a deterministic autoencoder's latent space. In this two-stage setup, the autoencoder compresses the image into its semantic features, which are then modeled with a deep VAE. With this method, the VAE avoids modeling the fine-grained details that constitute the majority of the image's code length, allowing it to focus on learning its structural components. We demonstrate the effectiveness of our two-stage approach, achieving a FID of 9.34 on the Image…
Carnegie Mellon University（卡内基梅隆大学）；UC San Diego（加州大学圣地亚哥分校）
We present a new generative autoencoder model with dual contradistinctive losses to improve generative autoencoder that performs simultaneous inference (reconstruction) and synthesis (sampling). Our model, named dual contradistinctive generative autoencoder (DC-VAE), integrates an instance-level discriminative loss (maintaining the instance-level fidelity for the reconstruction/synthesis) with a set-level adversarial loss (encouraging the set-level fidelity for there construction/synthesis), both being contradistinctive. Extensive experimental results by DC-VAE across different resolutions inc…
Beyond Gaussian Bottlenecks: Topologically Aligned Encoding of Vision-Transformer Feature Spaces
Department of Computer Engineering, Koç University, Istanbul, Turkey（科克大学计算机工程系，伊斯坦布尔，土耳其）；Department of Computer Engineering, Hacettepe University, Ankara, Turkey（哈恰塔佩大学计算机工程系，安卡拉，土耳其）；KUIS AI Research Center, Istanbul, Turkey（KUIS人工智能研究中心，伊斯坦布尔，土耳其）；Department of Electrical and Electronics Engineering, Koç University, Istanbul, Turkey（科克大学电气与电子工程系，伊斯坦布尔，土耳其）
A video prediction model that generalizes to diverse scenes would enable intelligent agents such as robots to perform a variety of tasks via planning with the model. However, while existing video prediction models have produced promising results on small datasets, they suffer from severe underfitting when trained on large and diverse datasets. To address this underfitting challenge, we first observe that the ability to train larger video prediction models is often bottlenecked by the memory constraints of GPUs or TPUs. In parallel, deep hierarchical latent variable models can produce higher qu…
Fraunhofer Institute for Computer Graphics Research IGD（弗劳恩霍夫计算机图形学研究所）；TU Darmstadt（达姆施塔特工业大学）
Investigating new methods of creating face morphing attacks is essential to foresee novel attacks and help mitigate them. Creating morphing attacks is commonly either performed on the image-level or on the representation-level. The representation-level morphing has been performed so far based on generative adversarial networks (GAN) where the encoded images are interpolated in the latent space to produce a morphed image based on the interpolated vector. Such a process was constrained by the limited reconstruction fidelity of GAN architectures. Recent advances in the diffusion autoencoder model…
Variational Rank Reduction Autoencoders for Generative Thermal Design
ESI Group-UZ Chair of the National Strategy on Artificial Intelligence（ESI集团-乌兹主席国家人工智能战略办公室）；Aragon Institute of Engineering Research (I3A)（阿拉贡工程研究所（I3A））；Universidad de Zaragoza（萨拉戈萨大学）；PIMM Lab（PIMM实验室）；ENSAM Institute of Technology（ENSAM技术研究所）
Warsaw University of Technology（华沙理工大学）；CERN（欧洲核子研究组织）；Tooploox
In this work, we propose a novel end-to-end sinkhorn autoencoder with noise generator for efficient data collection simulation. Simulating processes that aim at collecting experimental data is crucial for multiple real-life applications, including nuclear medicine, astronomy and high energy physics. Contemporary methods, such as Monte Carlo algorithms, provide high-fidelity results at a price of high computational cost. Multiple attempts are taken to reduce this burden, e.g. using generative approaches based on Generative Adversarial Networks or Variational Autoencoders. Although such methods …
We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and decoder networks, making our model an attractive candidate for applications where the encoding and/or decoding speed is critical. Additionally, VQ-VAE requires sampling an autoregressive model only in the compressed latent space, which is an order of magnitude faster than sampling i…
Deep learning (DL) methods where interpretability is intrinsically considered as part of the model are required to better understand the relationship of clinical and imaging-based attributes with DL outcomes, thus facilitating their use in the reasoning behind medical decisions. Latent space representations built with variational autoencoders (VAE) do not ensure individual control of data attributes. Attribute-based methods enforcing attribute disentanglement have been proposed in the literature for classical computer vision tasks in benchmark data. In this paper, we propose a VAE approach, th…
What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion
Shanghai Jiao Tong University（上海交通大学）；Alibaba Group（阿里巴巴集团）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；Beihang University（北航）；Sun Yat-sen University（中山大学）；Nankai University（南开大学）；Shanghai AI Laboratory（上海人工智能实验室）
Technical University of Denmark（丹麦技术大学）；University of Copenhagen（哥本哈根大学）；Twitter
We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. …
Autoencoders and generative neural network models have recently gained popularity in fluid mechanics due to their spontaneity and low processing time instead of high fidelity CFD simulations. Auto encoders are used as model order reduction tools in applications of fluid mechanics by compressing input high-dimensional data using an encoder to map the input space into a lower-dimensional latent space. Whereas, generative models such as Variational Auto-encoders (VAEs) and Generative Adversarial Networks (GANs) are proving to be effective in generating solutions to chaotic models with high 'rando…
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…
IRCAM - Sorbonne Université（IRCAM - 索邦大学）；CNRS UMR 9912（法国国家科学研究中心 UMR 9912）
Deep generative models applied to audio have improved by a large margin the state-of-the-art in many speech and music related tasks. However, as raw waveform modelling remains an inherently difficult task, audio generative models are either computationally intensive, rely on low sampling rates, are complicated to control or restrict the nature of possible signals. Among those models, Variational AutoEncoders (VAE) give control over the generation by exposing latent variables, although they usually suffer from low synthesis quality. In this paper, we introduce a Realtime Audio Variational autoE…
IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder
Institute of Trustworthy Embodied AI, Fudan University（复旦大学可信具身人工智能研究所）；Shanghai Innovation Institute（上海创新研究院）；University of Maryland, College Park（马里兰大学帕克分校）
Denoising Vision Transformer Autoencoder with Spectral Self-Regularization
Nanjing University（南京大学）；Kling Team, Kuaishou Technology（快手科技 Kling 团队）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shanghai Jiao Tong University（上海交通大学）；University of Science and Technology of China（中国科学技术大学）
Arizona State University（亚利桑那州立大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）
In this paper, we develop a Wasserstein autoencoder (WAE) with a hyperspherical prior for multimodal data in the application of inertial confinement fusion. Unlike a typical hyperspherical generative model that requires computationally inefficient sampling from distributions like the von Mis Fisher, we sample from a normal distribution followed by a projection layer before the generator. Finally, to determine the validity of the generated samples, we exploit a known relationship between the modalities in the dataset as a scientific constraint, and study different properties of the proposed mod…
Synthetic data for ratemaking: imputation-based methods vs adversarial networks and autoencoders
Department of Actuarial Science, Faculty of Business and Economics, University of Lausanne（卢塞恩大学商学院精算科学系）；Institute of Mathematics and Statistics, Faculty of Science and Technology, University of Tartu（塔尔图大学科学与技术学院数学与统计学研究所）
Synthetic Electrogram Generation with Variational Autoencoders for ECGI
Vicomtech, Basque Research and Technology Alliance（维克姆科技、巴斯克研究与技术联盟）；Universidad Rey Juan Carlos（雷昂卡洛斯大学）；eHealth Group, Biogipuzkoa Health Research Institute（eHealth集团、比斯克健康研究院）；ITACA Institute, Universitat Politècnica de València（ITACA研究所、巴塞罗那理工大学）
Spectral Complex Autoencoder Pruning: A Fidelity-Guided Criterion for Extreme Structured Channel Compression
School of Computer, Jiangsu University of Science and Technology（江苏科技大学计算机学院）；Department of Electrical and Computer Engineering, University of Nevada, Las Vegas（内华达大学拉斯维加斯分校电子与计算机工程系）
Real-time data collection and analysis in large experimental facilities present a great challenge across multiple domains, including high energy physics, nuclear physics, and cosmology. To address this, machine learning (ML)-based methods for real-time data compression have drawn significant attention. However, unlike natural image data, such as CIFAR and ImageNet that are relatively small-sized and continuous, scientific data often come in as three-dimensional data volumes at high rates with high sparsity (many zeros) and non-Gaussian value distribution. This makes direct application of popul…
Nautilus: Locality-aware Autoencoder for Scalable Mesh Generation
Nanyang Technological University（南洋理工大学）；Tencent Hunyuan（腾讯 Hunyuan）；South China University of Technology（华南理工大学）；The Hong Kong Polytechnic University（香港理工大学）；Hong Kong University of Science and Technology（香港科技大学）
