70 citations
- Arizona State UniversityUS2 papers
- Carnegie Mellon UniversityUS2 papers
- Robert Bosch (Germany)DE2 papers
- Technion – Israel Institute of TechnologyIL2 papers
- University of AmsterdamNL2 papers
- University of BaselCH2 papers
- University of FreiburgDE2 papers
- Department of Physics, Mathematics and InformaticsBY1 paper
- Indian Institute of Technology KanpurIN1 paper
- Karlsruhe Institute of TechnologyDE1 paper
- KU LeuvenBE1 paper
- Lawrence Livermore National LaboratoryUS1 paper
17 papers · 1 filter
Self-labeled Conditional GANs
Mehdi Noroozi
This paper introduces a novel and fully unsupervised framework for conditional GAN training in which labels are automatically obtained from data. We incorporate a clustering networ…
You Only Need Adversarial Supervision for Semantic Image Synthesis
Vadim Sushko, Edgar Schönfeld, Dan Zhang +3
Despite their recent successes, GAN models for semantic image synthesis still suffer from poor image quality when trained with only adversarial supervision. Historically, additiona…
High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds
Noémie Jaquier, Leonel Rozo
Despite the recent success of Bayesian optimization (BO) in a variety of applications where sample efficiency is imperative, its performance may be seriously compromised in setting…
Improving Augmentation and Evaluation Schemes for Semantic Image Synthesis
Prateek Katiyar, Anna Khoreva
Despite data augmentation being a de facto technique for boosting the performance of deep neural networks, little attention has been paid to developing augmentation strategies for…
Depth Completion with RGB Prior
Yuri Feldman, Yoel Shapiro, Dotan Di Castro
Depth cameras are a prominent perception system for robotics, especially when operating in natural unstructured environments. Industrial applications, however, typically involve re…
Qgraph-bounded Q-learning: Stabilizing Model-Free Off-Policy Deep Reinforcement Learning
Sabrina Hoppe, Marc Toussaint
In state of the art model-free off-policy deep reinforcement learning, a replay memory is used to store past experience and derive all network updates. Even if both state and actio…