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Related Experiment Video

Updated: Oct 23, 2025

Flying Insect Detection and Classification with Inexpensive Sensors

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

P-DIFF+: Improving learning classifier with noisy labels by Noisy Negative Learning loss.

QiHao Zhao¹, Wei Hu¹, Yangyu Huang²

¹Department of Computer Science and Technology, Beijing University of Chemical Technology, Beijing, China.

Neural Networks : the Official Journal of the International Neural Network Society

|August 21, 2021

Summary

This summary is machine-generated.

This study introduces P-DIFF+, a novel method for training deep neural network (DNN) classifiers with noisy labels. P-DIFF+ effectively mitigates overfitting by re-weighting samples based on their estimated cleanliness, improving model robustness.

Keywords:

Classification Deep neural networks Noisy Negative Learning loss Noisy labels Probability difference distribution

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Published on: September 25, 2021

Area of Science:

Machine Learning
Computer Science
Artificial Intelligence

Background:

Deep neural networks (DNNs) are prone to overfitting when trained on datasets with noisy labels.
Accurate classification is hindered by the DNNs' high capacity to memorize incorrect labels.

Purpose of the Study:

To present P-DIFF+, a simple yet effective training paradigm for DNN classifiers.
To alleviate the adverse impact of noisy labels on classifier performance.
To improve the robustness of DNNs against label noise.

Main Methods:

Developed P-DIFF+, a training paradigm utilizing probability difference distribution.
Implicitly estimated the probability of a training sample being clean.
Re-weighted training samples based on their estimated cleanliness.
Incorporated Noisy Negative Learning (NNL) loss for further sample re-weighting.

Main Results:

P-DIFF+ demonstrated superior performance compared to state-of-the-art sample selection methods.
Achieved good performance without requiring prior knowledge of the noise rate.
Effectively mitigated the adverse impact of noisy labels on DNN classifiers.

Conclusions:

P-DIFF+ offers a robust and effective solution for training DNNs with noisy labels.
The method enhances model generalization by addressing label noise.
P-DIFF+ presents a significant advancement in handling imperfect training data for deep learning.