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2025 ICML ICML 2025

LRA-QViT: Integrating Low-Rank Approximation and Quantization for Robust and Efficient Vision Transformers

Abstract

No abstract available. No abstract was captured for this paper when its proceedings were scraped - most often an older edition published without one online. How the corpus is built.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Computer Vision

Authors

Beom Jin Kang, Namjoon Kim, Hyun Kim

Topics

Machine Learning > Application Areas > Efficient Computing Deep Learning > Architectures > Transformers Computer Vision > Core AI > Computer Vision

Keywords

quantization knowledge distillation low-rank approximation parameter gflops reparameterizable branch-based lra
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