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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Related Experiment Video

Updated: Nov 16, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.7K

Deep Learning for Fashion Style Generation.

Shuhui Jiang, Jun Li, Yun Fu

    IEEE Transactions on Neural Networks and Learning Systems
    |February 26, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces novel deep neural network frameworks, FashionG and SC-FashionG, for generating fashion style images. These models effectively create single and mix-and-match styled clothing images, preserving both global design and local details.

    Related Experiment Videos

    Last Updated: Nov 16, 2025

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.7K

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Fashion Technology

    Background:

    • Generating realistic fashion images with desired styles is challenging.
    • Existing methods struggle to preserve global garment structure while incorporating diverse local styles.

    Purpose of the Study:

    • To develop deep neural network frameworks for generating fashion style images.
    • To enable single-style and mix-and-match style generation for clothing.
    • To address challenges in preserving global content and local details during style transfer.

    Main Methods:

    • Proposed FashionG framework for single-style generation.
    • Introduced SC-FashionG framework with spatial constraints for mix-and-match styles.
    • Utilized end-to-end feedforward neural networks with generators and discriminators.
    • Implemented alternating patch-global optimization with global and patch-based losses.

    Main Results:

    • FashionG successfully generates single-style fashion images.
    • SC-FashionG effectively produces mix-and-match styled clothing images.
    • Both frameworks preserve global clothing form and local style details.
    • SC-FashionG ensures styles are applied to specific garment areas.

    Conclusions:

    • The proposed FashionG and SC-FashionG frameworks demonstrate effectiveness in fashion style image generation.
    • These models offer advanced capabilities for both single and complex mix-and-match style applications.
    • The methods contribute to high-fidelity and controllable style transfer in fashion imagery.