Geometry-Preserving Encoder/Decoder in Latent Generative Models

Variational autoencoders with latent high-dimensional steady geometric flows for dynamics

arXiv:2410. 10137v5 Announce Type: replace Abstract: We develop Riemannian approaches to variational autoencoders (VAEs) for PDE-type ambient data with regularizing geometric latent dynamics, which we refer to as VAE-DLM, or VAEs with dynamical latent manifolds.

Variational Autoencoder Layer

arXiv:2606. 25900v1 Announce Type: new Abstract: Variational Autoencoders (VAEs) belong to a family of autoencoders with probabilistic properties, making them well suited for generating data by producing a smooth and continuous latent space.

Variational Autoencoder Layer

Variational Autoencoders (VAEs) belong to a family of autoencoders with probabilistic properties, making them well suited for generating data by producing a smooth and continuous latent space. Despite being introduced over a decade ago, the method continues to be widely adopted in both research and industry for diverse applications.

Generalization of Diffusion Models Arises with a Balanced Representation Space

arXiv:2512. 20963v3 Announce Type: replace Abstract: Diffusion models excel at generating high-quality, diverse samples, yet they risk memorizing training data when overfit to the training objective.

Learning on the Manifold: Unlocking Standard Diffusion Transformers with Representation Encoders

arXiv:2602. 10099v2 Announce Type: replace Abstract: Leveraging representation encoders for generative modeling offers a path for efficient, high-fidelity synthesis.

A Gentle Introduction to Autoencoders & Latent Space

Introduction Heavy computation is a well-known problem in various ML algorithms today, especially when generative AI is applied to text, images, and other unstructured data. One of the principal approaches to mitigate this problem is to compress input data into a lower-dimensional representation while preserving the main context.

Distilling Drifting Transformers with Representation Autoencoders

arXiv:2606. 15553v1 Announce Type: cross Abstract: Representation Autoencoders (RAEs) have improved diffusion and flow models by semantically richer latent space owing to the strongly label-wise clustered DINO features in the pretrained encoders.

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds

arXiv:2608. 04827v1 Announce Type: cross Abstract: We introduce the Intrinsic Hybrid Latent Diffusion Model (ILDM), a generative framework that integrates probabilistic dimensionality reduction with geometry-aware diffusion on unknown manifolds.

Variational lossy autoencoder

PixRestore: Unified Image Restoration via Pixel Diffusion Transformer

Unified image restoration (UIR) aims to recover high-quality (HQ) content from low-quality (LQ) images with different degradations using a single model. Most recent methods adapt large pretrained text-to-image (T2I) latent diffusion models for their strong capacity and generative priors.

UDT: Reconciling U-Nets and Diffusion Transformers with Data-Adaptive Token Reduction

arXiv:2608. 01298v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) have emerged as a core architecture in generative modeling due to their scalability and adaptability to multimodal tasks.

STREAM: Stochastic Riemannian Flow Matching with Anisotropic Decoder for Digital Histopathology Image Generation

arXiv:2606. 07036v1 Announce Type: cross Abstract: Synthetic histopathology image generation addresses critical challenges in computational pathology, including patient privacy and the growing need for large-scale training data for foundation models.