Results 81 to 90 of about 39,370 (305)
Generative Multi-Adversarial Networks
CoRR, 2016 Generative adversarial networks (GANs) are a framework for producing a generative model by way of a two-player minimax game. In this paper, we propose the \emph{Generative Multi-Adversarial Network} (GMAN), a framework that extends GANs to multiple discriminators.
Ishan P. Durugkar +2 more
openaire +3 more sources
Quantum generative adversarial networks [PDF]
Physical Review A, 2018 10 pages, 8 ...
Pierre-Luc Dallaire-Demers +1 more
openaire +2 more sources
Information Transmission Strategies for Self‐Organized Robotic Aggregation
Advanced Robotics Research, EarlyView.In this review, we discuss how information transmission influences the neighbor‐based self‐organized aggregation of swarm robots. We focus specifically on local interactions regarding information transfer and categorize previous studies based on the functions of the information exchanged.
Shu Leng +5 more
wiley +1 more source
Generative Adversarial Networks Unlearning
IEEE Transactions on Dependable and Secure ComputingAs machine learning continues to develop, and data misuse scandals become more prevalent, individuals are becoming increasingly concerned about their personal information and are advocating for the right to remove their data. Machine unlearning has emerged as a solution to erase training data from trained machine learning models. Despite its success in
Hui Sun +3 more
openaire +2 more sources
Generative Adversarial Mapping Networks
CoRR, 2017 9 pages, 7 ...
Jianbo Guo, Guangxiang Zhu, Jian Li
openaire +2 more sources
Slimmable Generative Adversarial Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Generative adversarial networks (GANs) have achieved remarkable progress in recent years, but the continuously growing scale of models make them challenging to deploy widely in practical applications. In particular, for real-time generation tasks, different devices require generators of different sizes due to varying computing power.
Liang Hou +5 more
openaire +2 more sources
Training Generative Adversarial Networks via Stochastic Nash Games
, 2023 Generative adversarial networks (GANs) are a class of generative models with two antagonistic neural networks: a generator and a discriminator. These two neural networks compete against each other through an adversarial process that can be modeled as a ...
Franci, Barbara +5 more
core +1 more source
Continual Learning for Multimodal Data Fusion of a Soft Gripper
Advanced Robotics Research, EarlyView.Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley +1 more source
Mathematics, 2023 In this paper, we propose a novel network, self-attention generative adversarial network with blur and memory (BaMSGAN), for generating anime faces with improved clarity and faster convergence while retaining the capacity for continuous learning ...
Xu Li +4 more
doaj +1 more source
Advanced Robotics Research, EarlyView.This work presents a state‐adaptive Koopman linear quadratic regulator framework for real‐time manipulation of a deformable swab tool in robotic environmental sampling. By combining Koopman linearization, tactile sensing, and centroid‐based force regulation, the system maintains stable contact forces and high coverage across flat and inclined surfaces.
Siavash Mahmoudi +2 more
wiley +1 more source