Perceptual Loss-Constrained Adversarial Autoencoder Networks for Hyperspectral Unmixing
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3144327
Abstract: Recently, the use of a deep autoencoder-based method in blind spectral unmixing has attracted great attention as the method can achieve superior performance. However, most autoencoder-based unmixing methods use non-structured reconstruction loss to train networks,… read more here.
Keywords: loss constrained; adversarial autoencoder; loss; perceptual loss ... See more keywords
Robust Decision Making for Autonomous Vehicles at Highway On-Ramps: A Constrained Adversarial Reinforcement Learning Approach
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2022.3229518
Abstract: Reinforcement learning has demonstrated its potential in a series of challenging domains. However, many real-world decision making tasks involve unpredictable environmental changes or unavoidable perception errors that are often enough to mislead an agent into… read more here.
Keywords: constrained adversarial; autonomous vehicles; decision making; reinforcement learning ... See more keywords
CAT: Constrained Adversarial Training for Anatomically-plausible Semi-supervised Segmentation.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on medical imaging"
DOI: 10.1109/tmi.2023.3243069
Abstract: Deep learning models for semi-supervised medical image segmentation have achieved unprecedented performance for a wide range of tasks. Despite their high accuracy, these models may however yield predictions that are considered anatomically impossible by clinicians.… read more here.
Keywords: semi supervised; anatomically plausible; segmentation; constrained adversarial ... See more keywords