Data-free Knowledge Distillation for Fine-grained Visual Categorization

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