Results 61 to 70 of about 116,908 (254)

Generating Adversarial Examples with Adversarial Networks [PDF]

open access: yesProceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018
Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high ...
Xiao, Chaowei   +5 more
openaire   +2 more sources

Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space

open access: yesAdvanced Robotics Research, EarlyView.
A quadruped robot masters dynamic jumps through constrained spaces with animal‐inspired moves and intelligent vision control. This hierarchical learning approach combines imitation of biological agility with real‐time trajectory planning. Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating ...
Zeren Luo   +6 more
wiley   +1 more source

Stable Imitation of Multigait and Bipedal Motions for Quadrupedal Robots Over Uneven Terrains

open access: yesAdvanced Robotics Research, EarlyView.
How are quadrupedal robots empowered to execute complex navigation tasks, including multigait and bipedal motions? Challenges in stability and real‐world adaptation persist, especially with uneven terrains and disturbances. This article presents an imitation learning framework that enhances adaptability and robustness by incorporating long short‐term ...
Erdong Xiao   +3 more
wiley   +1 more source

BaMSGAN: Self-Attention Generative Adversarial Network with Blur and Memory for Anime Face Generation

open access: yesMathematics, 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

Generative Adversarial Networks: Recent Developments [PDF]

open access: yes, 2019
10 ...
Zamorski, Maciej   +3 more
openaire   +2 more sources

Multi-agent Diverse Generative Adversarial Networks [PDF]

open access: yes2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
This is an updated version of our CVPR'18 paper with the same title.
Arnab Ghosh   +4 more
openaire   +2 more sources

Information Transmission Strategies for Self‐Organized Robotic Aggregation

open access: yesAdvanced 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

Continual Learning for Multimodal Data Fusion of a Soft Gripper

open access: yesAdvanced 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

A Review of the Research and Development of Adversarial Generative Networks in Interior Graphic Design [PDF]

open access: yesITM Web of Conferences
This study provides a comprehensive overview of the research and development of adversarial generative networks in interior graphic design. With the continuous development of adversarial generative networks, the level of Generative Adversarial Networks ...
Yang Haonan
doaj   +1 more source

Adaptive Density Estimation for Generative Models [PDF]

open access: yes, 2019
Unsupervised learning of generative models has seen tremendous progress over recent years, in particular due to generative adversarial networks (GANs), variational autoencoders, and flow-based models.
Alahari, Karteek   +4 more
core   +2 more sources

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