Generating context-specific sports training plans by combining generative adversarial networks.
Personalized sports training plans are essential for addressing individual athlete needs, but traditional methods often need to integrate diverse data types, limiting adaptability and effectiveness. Existing machine learning (ML) and rule-based approaches cannot dynamically generate context-specific...
Saved in:
Main Authors: | Juquan Tan, Jingwen Chen |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2025-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0318321 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial Networks
by: Linyan Li, et al.
Published: (2020-01-01) -
Hybrid Quantum Cycle Generative Adversarial Network for Small Molecule Generation
by: Matvei Anoshin, et al.
Published: (2024-01-01) -
A Reliable Generative Adversarial Network Approach for Climate Downscaling and Weather Generation
by: Neelesh Rampal, et al.
Published: (2025-01-01) -
A Multi-Index Generative Adversarial Network for Tool Wear Detection with Imbalanced Data
by: Guokai Zhang, et al.
Published: (2020-01-01) -
Unsupervised Text Embedding Space Generation Using Generative Adversarial Networks for Text Synthesis
by: Jun-Min Lee, et al.
Published: (2023-10-01)