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Related Experiment Video

Updated: Oct 26, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks.

Yuesong Tian, Li Shen, Li Shen

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 26, 2021
    PubMed
    Summary

    This study introduces alphaGAN, a novel framework for automatically searching optimal network architectures for Generative Adversarial Networks (GANs). AlphaGAN efficiently finds high-performing GAN architectures, improving image generation quality and robustness.

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    Last Updated: Oct 26, 2025

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

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Vision

    Background:

    • Generative Adversarial Networks (GANs) face challenges in performance and robustness due to complex minimax game formulations.
    • Automated architecture search offers a promising direction to enhance GANs.

    Purpose of the Study:

    • To develop a novel, fully differentiable search framework, alphaGAN, for optimizing GAN network architectures.
    • To efficiently explore a vast search space of potential GAN configurations.

    Main Methods:

    • Formulated architecture search as a bi-level minimax optimization problem within a differentiable framework.
    • Employed a first-order approach to alternately minimize objectives for architecture and network parameters.
    • Integrated automated architecture search with GAN training.

    Main Results:

    • Achieved high-performing GAN architectures in just 3 GPU hours on a search space of 2x10^11 configurations.
    • Significantly improved Fréchet Inception Distance (FID) scores on benchmark datasets (CelebA, LSUN-church, FFHQ) when applied to StyleGAN2.
    • Demonstrated relative FID improvements of 3%-26% over baseline architectures.

    Conclusions:

    • AlphaGAN provides an efficient and effective method for discovering superior GAN architectures.
    • The framework enhances GAN performance and robustness, advancing generative modeling.
    • The study offers insights into architecture search for generative models.