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Updated: Dec 10, 2025

A User-friendly and Powerful R Analysis of Large-scale Datasets

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

Are open set classification methods effective on large-scale datasets?

Ryne Roady¹, Tyler L Hayes¹, Ronald Kemker¹

¹Rochester Institute of Technology, Rochester, NY, United States of America.

|September 4, 2020

Summary

This summary is machine-generated.

This study compares open set recognition methods for deep learning models. Input perturbation and temperature scaling are most effective for large-scale datasets, outperforming complex training strategies.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Supervised classification models typically assume identical train/test data distributions and known classes.
Deployed classifiers need to identify unknown inputs (out-of-distribution detection/open set recognition).
Existing convolutional neural network approaches include inference methods and feature space regularization.

Purpose of the Study:

To explore the relationship between inference and regularization methods in open set recognition.
To compare their performance on large-scale datasets with numerous categories.
To identify optimal combinations for robust unknown input detection.

Main Methods:

Utilized the ImageNet ILSVRC-2012 dataset for large-scale classification.
Investigated combinations of feature space regularization and specialized inference methods.
Evaluated performance across open set classification problems of varying difficulty.

Main Results:

Input perturbation and temperature scaling demonstrated superior performance on large datasets, irrespective of regularization.
Advanced regularization schemes improved performance with baseline inference but showed less benefit with advanced inference methods.
The effectiveness of training paradigms diminished when sophisticated inference techniques were employed for open set detection.

Conclusions:

For large-scale open set recognition, input perturbation and temperature scaling are key inference strategies.
Complex training regularization offers limited advantages when advanced inference methods are used.
Future research should focus on optimizing inference techniques for practical, large-scale deployment.