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Related Experiment Video

Updated: Nov 6, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Tweaking Deep Neural Networks.

Jinwook Kim, Heeyong Yoon, Min-Soo Kim

IEEE Transactions on Pattern Analysis and Machine Intelligence

|May 12, 2021

Summary

This summary is machine-generated.

This study introduces a synaptic join method to enhance deep neural network (DNN) performance by selectively improving specific class accuracies. The technique controllably boosts targeted class performance without compromising overall accuracy.

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Deep neural networks (DNNs) optimize for overall accuracy on training data.
Improving accuracy for specific, valuable classes in DNNs is challenging.
Existing methods struggle to selectively enhance performance for niche or problematic classes.

Purpose of the Study:

To develop a novel method for selectively improving DNN accuracies for specific classes.
To introduce a controllable approach for DNN performance enhancement.
To address the limitations of standard DNN training in targeting specific class improvements.

Main Methods:

Propose the synaptic join method to modify DNN architecture.
Add and selectively train new synapses connecting intermediate hidden layers to the output layer.
Evaluate candidate synapses based on hidden neuron and output neuron performance and weight distributions.

Main Results:

The synaptic join method effectively improves accuracies of targeted classes.
Performance enhancement for specific classes is achieved in a controllable manner.
The method demonstrates efficacy in boosting accuracy for selected problematic or valuable classes.

Conclusions:

The synaptic join method offers a viable solution for targeted DNN accuracy improvement.
This technique allows for fine-tuning DNNs to meet specific application needs.
Controllable enhancement of specific class accuracies is achievable with the proposed method.