Work

Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation

Year: 2024

Type: article

Abstract: This paper introduces the MMTADAN, an innovative algorithm designed to enhance cross-domain image classification. By integrating multi-scale feature extraction with Taylor series-based detail enhancement and adversarial domain adaptation, the MMTADAN effectively aligns features between the source and target domains. The proposed approach addresses the critical challenge of generalizing classification models across diverse datasets, demonstrating significant improvements in performance. The findings suggest that retaining essential image details through multi-scale extraction and Taylor series enhancement can lead to better classification outcomes, making the MMTADAN a valuable contribution to the field of image classification.

Source: Applied Sciences

DOI: 10.3390/app142210531

Authors: Yu Guo, Ziyi Cheng, Yuanlong Zhang, Gaoxuan Wang, Jundong Zhang

Institutions: Dalian Maritime University, University of Manchester, University of Nottingham, China University of Geosciences

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funder: National Key Scientific Instrument and Equipment Development Projects of China