Latent Space
Latent space refers to a lower-dimensional representation of high-dimensional data, aiming to capture essential features while reducing computational complexity and improving interpretability. Current research focuses on developing efficient algorithms and model architectures, such as variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models, to learn and manipulate these latent spaces for tasks ranging from anomaly detection and image generation to controlling generative models and improving the efficiency of autonomous systems. This work has significant implications across diverse fields, enabling advancements in areas like drug discovery, autonomous driving, and cybersecurity through improved data analysis, model efficiency, and enhanced control over generative processes.
Papers - Page 7
Optimizing 3D Geometry Reconstruction from Implicit Neural Representations
Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective
Improved Anomaly Detection through Conditional Latent Space VAE Ensembles
Reclaiming the Source of Programmatic Policies: Programmatic versus Latent Spaces
LatentBKI: Open-Dictionary Continuous Mapping in Visual-Language Latent Spaces with Quantifiable Uncertainty
Converging to a Lingua Franca: Evolution of Linguistic Regions and Semantics Alignment in Multilingual Large Language Models
A Unified Framework for Forward and Inverse Problems in Subsurface Imaging using Latent Space Translations