Data-free Knowledge Distillation for Fine-grained Visual Categorization

• URL: http://arxiv.org/abs/2404.12037v1
• Date: Thu, 18 Apr 2024 09:44:56 GMT
• Title: Data-free Knowledge Distillation for Fine-grained Visual Categorization
• Authors: Renrong Shao, Wei Zhang, Jianhua Yin, Jun Wang,
• Abstract summary: We propose an approach called DFKD-FGVC that extends DFKD to fine-grained visual categorization(FGVC) tasks. We evaluate our approach on three widely-used FGVC benchmarks (Aircraft, Cars196, and CUB200) and demonstrate its superior performance.
• Score: 9.969720644789781
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 distillation framework with attention generator, mixed high-order attention distillation, and semantic feature contrast learning. Specifically, we introduce a spatial-wise attention mechanism to the generator to synthesize fine-grained images with more details of discriminative parts. We also utilize the mixed high-order attention mechanism to capture complex interactions among parts and the subtle differences among discriminative features of the fine-grained categories, paying attention to both local features and semantic context relationships. Moreover, we leverage the teacher and student models of the distillation framework to contrast high-level semantic feature maps in the hyperspace, comparing variances of different categories. We evaluate our approach on three widely-used FGVC benchmarks (Aircraft, Cars196, and CUB200) and demonstrate its superior performance.

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