Work

Selective Classification Under Distribution Shifts

Year: 2024

Type: article

Abstract: in the SC literature. We focus on non-training-based confidence-score functions for generalized SC on deep learning (DL) classifiers, and propose two novel margin-based score functions. Through extensive analysis and experiments, we show that our proposed score functions are more effective and reliable than the existing ones for generalized SC on a variety of classification tasks and DL classifiers. The code is available at https://github.com/sun-umn/sc_with_distshift.

Source: PubMed

DOI: 10.48550/arxiv.2405.05160

Authors: Liang, Hengyue, Peng, Le, Sun, Ju

Institution: University of Minnesota

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green