Deep learning in virtual try-on: A comprehensive survey
Virtual try-on technology has gained significant importance in the retail industry due to its
potential to transform the way customers interact with products and make purchase …
potential to transform the way customers interact with products and make purchase …
Image-based virtual try-on: A survey
Image-based virtual try-on aims to synthesize a naturally dressed person image with a
clothing image, which revolutionizes online shop** and inspires related topics within …
clothing image, which revolutionizes online shop** and inspires related topics within …
Double policy estimation for importance sampling in sequence modeling-based reinforcement learning
Offline reinforcement learning aims to utilize datasets of previously gathered environment-
action interaction records to learn a policy without access to the real environment. Recent …
action interaction records to learn a policy without access to the real environment. Recent …
OAM modes classification and demultiplexing via Fourier optical neural network
Here, we present a free-space optical communication system, adept at managing alignment
deviations and the challenges posed by the atmospheric turbulence in the transmission of …
deviations and the challenges posed by the atmospheric turbulence in the transmission of …
Orbital angular momentum beams multiplexing and demultiplexing using hybrid optical-electronic neural network
Recent advancements in optical communications have explored the use of spatially
structured beams, especially orbital angular momentum (OAM) beams, to achieve high …
structured beams, especially orbital angular momentum (OAM) beams, to achieve high …
Advanced 4f-based free-space optical system
Here, we introduce a framework leveraging a free-space optical system based on the 4f
configuration, inspired by the SWIFFT algorithms, designed to significantly enhance the …
configuration, inspired by the SWIFFT algorithms, designed to significantly enhance the …
Decision-Making via Optimization in Challenging Environment: Bayesian and Multi-Agent Reinforcement Learning Perspectives
Y Mei - 2025 - search.proquest.com
Decision-making is essential in many real-world problems, where the goal is to select
actions that optimize long-term outcomes under uncertainty. From tuning hyperparameters to …
actions that optimize long-term outcomes under uncertainty. From tuning hyperparameters to …
Effective Value Function Factorization and Exploration in Multi-Agent Reinforcement Learning
H Zhou - 2024 - search.proquest.com
The evolution of computer science has profoundly impacted decision-making processes
across diverse domains, culminating in the development of artificial intelligence (AI) and …
across diverse domains, culminating in the development of artificial intelligence (AI) and …
Reconfigurable complex convolution module based optical data compression and hashing algorithm
Here, we explores the forefront of optical dynamic real-time signal processing with the
introduction of a Reconfigurable Complex Convolution Module (RCCM), leveraging …
introduction of a Reconfigurable Complex Convolution Module (RCCM), leveraging …