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 19
Latent Space Explorer: Visual Analytics for Multimodal Latent Space Exploration
Bum Chul Kwon, Samuel Friedman, Kai Xu, Steven A Lubitz, Anthony Philippakis, Puneet Batra, Patrick T Ellinor, Kenney NgGenerative models for visualising abstract social processes: Guiding streetview image synthesis of StyleGAN2 with indices of deprivation
Aleksi KnuutilaTowards Aligned Canonical Correlation Analysis: Preliminary Formulation and Proof-of-Concept Results
Biqian Cheng, Evangelos E. Papalexakis, Jia ChenText Attribute Control via Closed-Loop Disentanglement
Lei Sha, Thomas Lukasiewicz
Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation
Hang Li, Chengzhi Shen, Philip Torr, Volker Tresp, Jindong GuTLControl: Trajectory and Language Control for Human Motion Synthesis
Weilin Wan, Zhiyang Dou, Taku Komura, Wenping Wang, Dinesh Jayaraman, Lingjie LiuScalable Label Distribution Learning for Multi-Label Classification
Xingyu Zhao, Yuexuan An, Lei Qi, Xin Geng
Identifying Linear Relational Concepts in Large Language Models
David Chanin, Anthony Hunter, Oana-Maria CamburuSelf-Supervised Disentanglement by Leveraging Structure in Data Augmentations
Cian Eastwood, Julius von Kügelgen, Linus Ericsson, Diane Bouchacourt, Pascal Vincent, Bernhard Schölkopf, Mark Ibrahim
Differentiable VQ-VAE's for Robust White Matter Streamline Encodings
Andrew Lizarraga, Brandon Taraku, Edouardo Honig, Ying Nian Wu, Shantanu H. Joshi2D Image head pose estimation via latent space regression under occlusion settings
José Celestino, Manuel Marques, Jacinto C. Nascimento, João Paulo Costeira