Joint Post-Training Quantization of Vision Transformers with Learned Prompt-Guided Data Generation

Researchers introduced a framework for joint quantization of Vision Transformers that optimizes all layers without labeled data, achieving state-of-the-art accuracy in low-bit settings and demonstrating potential for efficient edge deployment. The study also presents a data-free calibration strategy using Stable Diffusion Turbo guided by learned prompts, which matches real-data performance and encourages diversity in generated samples.

Read the full article at arXiv cs.CV (Vision)

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