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Related Experiment Video

Updated: May 24, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Data Cleansing for GANs.

Naoyuki Terashita, Hiroki Ohashi, Satoshi Hara

IEEE Transactions on Neural Networks and Learning Systems

|March 3, 2025

Summary

This summary is machine-generated.

This study introduces a novel method to identify and remove harmful training data for generative adversarial networks (GANs), significantly improving GAN performance on various metrics.

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Published on: March 18, 2019

Area of Science:

Artificial Intelligence
Machine Learning
Computer Vision

Background:

Generative Adversarial Networks (GANs) are powerful tools for various generative tasks.
Improving GAN performance requires effective data curation strategies.
Existing methods for identifying harmful training instances are not directly applicable to GANs due to their unique training dynamics.

Purpose of the Study:

To develop a unified approach for improving GAN performance by identifying and removing harmful training instances.
To address the limitations of previous methods in the context of GANs.

Main Methods:

Proposed influence estimation using the Jacobian of gradients between the generator and discriminator.
Developed an instance evaluation scheme based on expected changes in GAN evaluation metrics (e.g., Inception Score).

Main Results:

Successfully identified harmful training instances in GANs.
Demonstrated significant improvements in generative performance across various GAN evaluation metrics after removing identified instances.

Conclusions:

The proposed method effectively identifies and removes detrimental training data for GANs.
This approach offers a unified strategy to enhance GAN performance across diverse generative applications.