Results 111 to 120 of about 129,070 (253)

Knowledge Transfer in Deep Reinforcement Learning via an RL-Specific GAN-Based Correspondence Function

IEEE Access
Deep reinforcement learning has demonstrated superhuman performance in complex decision-making tasks, but it struggles with generalization and knowledge reuse—key aspects of true intelligence. This article introduces a novel approach that modifies
Marko Ruman, Tatiana V. Guy
doaj   +1 more source

A Holographic Sensor‐Integrated Deep Learning Framework for Noninvasive Assessment of Stored Red Blood Cell Quality

Advanced Sensor Research, EarlyView.
A holographic sensing‐integrated deep learning platform enables real‐time, label‐free assessment of red blood cell (RBC) quality during storage. By combining diffusion model‐based data augmentation and self‐supervised pretraining, the framework achieves high segmentation accuracy with minimal data.
Seonghwan Park, Hyunbin An, Abdur Rehman, Inkyu Moon   +3 more
wiley   +1 more source

Latent Deep Space: Generative Adversarial Networks (GANs) in the Sciences [PDF]

Media + Environment, 2021
The recent spectacular success of machine learning in the sciences points to the emergence of a new artificial intelligence trading zone. The epistemological implications of this trading zone, however, have so far not been studied in depth.
Fabian Offert
doaj  

Leveraging Transfer Learning to Overcome Data Limitations in Czochralski Crystal Growth

Advanced Theory and Simulations, EarlyView.
A data‐driven framework combining Computational Fluid Dynamics (CFD) simulations and machine learning is proposed to model and optimize Czochralski crystal growth. Using different transfer learning strategies (Warm Start, Merged Training, and Hyperparameter Transfer) the study demonstrates improved predictions for Ge and GaAs growth from Si‐trained ...
Milena Petkovic, Natasha Dropka, Xia Tang, Janina Zittel   +3 more
wiley   +1 more source

Generative adversarial synthetic neighbors-based unsupervised anomaly detection

Scientific Reports
Anomaly detection is crucial for the stable operation of mechanical systems, securing financial transactions, and ensuring network security, among other critical areas.
Lan Chen, Hong Jiang, Lizhong Wang, Jun Li, Manhua Yu, Yong Shen, Xusheng Du   +6 more
doaj   +1 more source

Multi-Source Medical Image Fusion Based on Wasserstein Generative Adversarial Networks

IEEE Access, 2019
In this paper, we propose the medical Wasserstein generative adversarial networks (MWGAN), an end-to-end model, for fusing magnetic resonance imaging (MRI) and positron emission tomography (PET) medical images.
Zhiguang Yang, Youping Chen, Zhuliang Le, Fan Fan, Erting Pan   +4 more
doaj   +1 more source

Generative Adversarial Networks: Recent Developments [PDF]

, 2019
10 ...
Maciej Zięba, Maciej Zamorski, Adrian Zdobylak, Jerzy Świątek   +3 more
openaire   +3 more sources

Cardiac disease diagnosis based on GAN in case of missing data.

PLoS ONE
In daily life, two common algorithms are used for collecting medical disease data: data integration of medical institutions and questionnaires. However, these statistical methods require collecting data from the entire research area, which consumes a ...
Xing Chen, Na Zhang, Xiaohui Yang, Chunyan Wang, Qi Na, Tianyun Luan, Wendi Zhu, Chenjie Zhang, Chao Yang   +8 more
doaj   +1 more source

Deep Convolutional Generative Adversarial Networks in Image-Based Android Malware Detection

Computers
The recent advancements in generative adversarial networks have showcased their remarkable ability to create images that are indistinguishable from real ones.
Francesco Mercaldo, Fabio Martinelli, Antonella Santone   +2 more
doaj   +1 more source

Generative Adversarial Networks Unlearning

IEEE Transactions on Dependable and Secure Computing
As 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, Tianqing Zhu, Wenhan Chang, Wanlei Zhou   +3 more
openaire   +2 more sources