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Related Experiment Video

Updated: Nov 12, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Addi-Reg: A Better Generalization-Optimization Tradeoff Regularization Method for Convolutional Neural Networks.

Yao Lu, Zheng Zhang, Guangming Lu

IEEE Transactions on Cybernetics

|March 22, 2021

Summary

This summary is machine-generated.

We introduce Addi-Reg, a novel noise injection method for convolutional neural networks (CNNs). Addi-Reg improves generalization and optimization by adaptively generating noise, outperforming existing multiplicative regularization techniques.

Related Experiment Videos

Last Updated: Nov 12, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Deep Learning
Computer Vision
Machine Learning

Background:

Generating noise in intermediate features is crucial for improving convolutional neural network (CNN) generalization.
Existing multiplicative regularization (Multi-Reg) methods focus on generalization but often neglect optimization, leading to slow convergence and unstable training.
Current Multi-Reg methods lack flexibility and universality, being limited to specific network architectures.

Purpose of the Study:

To explore the nature of noise generation in intermediate features of popular CNNs.
To address the limitations of Multi-Reg methods concerning optimization, flexibility, and universality.
To propose a novel regularization method that enhances both generalization and optimization in CNNs.

Main Methods:

Experimentally and theoretically analyzing noise generation in CNN intermediate features.
Demonstrating the transformation of feature-space noise injection to input-space noise generation via Mini-batch in Mini-batch (MiM) sampling.
Proposing the Additional Regularization (Addi-Reg) method, which adaptively generates noise with low dependence on intermediate features using specialized mechanisms.

Main Results:

Multiplicative noise generation can degenerate optimization due to high dependence on intermediate features.
Addi-Reg stabilizes the training process by adaptively learning layer-specific noise distributions.
Addi-Reg demonstrates superior flexibility, universality, generalization performance, and convergence speed compared to state-of-the-art Multi-Reg methods.

Conclusions:

Addi-Reg offers a more robust and versatile approach to regularization in CNNs.
The proposed method effectively balances generalization and optimization, leading to improved network performance.
Addi-Reg represents a significant advancement over existing multiplicative regularization techniques for CNNs.