The paper explores the efficiency of model quantization for face recognition by fine-tuning the model with a significantly smaller dataset compared to traditional methods. By incorporating evaluation-based metric loss, the authors achieve state-of-the-art accuracy on the IJB-C dataset. The study highlights the transformative power of efficient training approaches in face recognition models, emphasizing the importance of optimizing performance with minimal resources.
The research challenges the notion that extensive datasets are essential for successful model compression in face recognition. By focusing on small data and training times, the authors demonstrate significant improvements in efficiency without compromising accuracy. The proposed method showcases a new paradigm in model quantization, offering practical implications for real-world applications.
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ข้อมูลเชิงลึกที่สำคัญจาก
by William Gaza... ที่ arxiv.org 02-29-2024
https://arxiv.org/pdf/2402.18163.pdfสอบถามเพิ่มเติม