April 19, 2024, 4:45 a.m. | Renrong Shao, Wei Zhang, Jianhua Yin, Jun Wang

cs.CV updates on arXiv.org arxiv.org

arXiv:2404.12037v1 Announce Type: new
Abstract: Data-free knowledge distillation (DFKD) is a promising approach for addressing issues related to model compression, security privacy, and transmission restrictions. Although the existing methods exploiting DFKD have achieved inspiring achievements in coarse-grained classification, in practical applications involving fine-grained classification tasks that require more detailed distinctions between similar categories, sub-optimal results are obtained. To address this issue, we propose an approach called DFKD-FGVC that extends DFKD to fine-grained visual categorization~(FGVC) tasks. Our approach utilizes an adversarial …

abstract applications arxiv classification compression cs.cv data distillation fine-grained free knowledge practical privacy restrictions security tasks type visual

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Business Data Analyst

@ Alstom | Johannesburg, GT, ZA