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Related Experiment Video

Updated: Mar 8, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Unifying Adversarial Training Algorithms with Data Gradient Regularization.

Alexander G Ororbia Ii¹, Daniel Kifer², C Lee Giles³

¹Pennsylvania State University, University Park, PA 16802, U.S.A. agol09@ist.psu.edu.

Neural Computation

|January 18, 2017

Summary

This summary is machine-generated.

DataGrad unifies adversarial training methods for deep neural networks, simplifying prior work. This framework enhances robustness against adversarial attacks, outperforming other regularization techniques.

Related Experiment Videos

Last Updated: Mar 8, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Machine Learning
Deep Learning
Artificial Intelligence

Background:

Adversarial training of deep neural networks (DNNs) has seen various proposals, including gradient modification, mixed-data training, and contractive penalties.
These diverse methods often lack a unifying theoretical framework, hindering systematic comparison and extension.

Purpose of the Study:

To introduce a general framework, DataGrad, that unifies and simplifies existing adversarial training methods for DNNs.
To demonstrate the effectiveness of DataGrad, particularly its deep gradient regularization, against adversarial examples and compare it with other regularization techniques.

Main Methods:

Proposing the DataGrad framework as a general, regularized objective function for adversarial training.
Viewing DataGrad as a deep extension of the layerwise contractive autoencoder penalty.
Implementing and evaluating DataGrad with L1 and L2 regularization flavors, and exploring extensions like multitask cues.

Main Results:

DataGrad successfully unifies various previous adversarial training proposals under a single objective.
Deep gradient regularization within DataGrad demonstrated superior performance over classical L1, L2, and multitask regularization on both clean and adversarial datasets.
Combining multitask optimization with DataGrad adversarial training yielded the most robust performance.

Conclusions:

DataGrad offers a simplified and unified approach to adversarial training for DNNs.
The DataGrad framework, especially with deep gradient regularization and multitask integration, significantly enhances model robustness against adversarial attacks.