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
Multimodal Pathology Image Search Between H&E Slides and Multiplexed Immunofluorescent Images
Amir Hajighasemi, MD Jillur Rahman Saurav, Mohammad S Nasr, Jai Prakash Veerla, Aarti Darji, Parisa Boodaghi Malidarreh, Michael Robben, Helen H Shang, Jacob M Luber
Happy People -- Image Synthesis as Black-Box Optimization Problem in the Discrete Latent Space of Deep Generative Models
Steffen Jung, Jan Christian Schwedhelm, Claudia Schillings, Margret Keuper
Interpretable Alzheimer's Disease Classification Via a Contrastive Diffusion Autoencoder
Ayodeji Ijishakin, Ahmed Abdulaal, Adamos Hadjivasiliou, Sophie Martin, James Cole
Learning nonparametric latent causal graphs with unknown interventions
Yibo Jiang, Bryon Aragam
Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming
Xinlei Niu, Christian Walder, Jing Zhang, Charles Patrick Martin
Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information
Kun Zhao, Bohao Yang, Chenghua Lin, Wenge Rong, Aline Villavicencio, Xiaohui Cui
On convex decision regions in deep network representations
Lenka Tětková, Thea Brüsch, Teresa Karen Scheidt, Fabian Martin Mager, Rasmus Ørtoft Aagaard, Jonathan Foldager, Tommy Sonne Alstrøm, Lars Kai Hansen
Sound Design Strategies for Latent Audio Space Explorations Using Deep Learning Architectures
Kıvanç Tatar, Kelsey Cotton, Daniel Bisig
A Virtual Reality Tool for Representing, Visualizing and Updating Deep Learning Models
Hannes Kath, Bengt Lüers, Thiago S. Gouvêa, Daniel Sonntag
A Deep Generative Model for Interactive Data Annotation through Direct Manipulation in Latent Space
Hannes Kath, Thiago S. Gouvêa, Daniel Sonntag