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Semantic Alignment and Reinforcement for Data-Free Quantization of Vision Transformers

arXiv — cs.CV•Monday, November 3, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
A recent study on data-free quantization (DFQ) for Vision Transformers (ViTs) highlights significant advancements in model quantization without the need for real data, which is crucial for maintaining data security and privacy. The research addresses two major challenges: semantic distortion and inadequacy in existing DFQ methods. By improving the alignment of synthetic images with real-world semantics, this work not only enhances the performance of ViTs but also paves the way for safer AI applications. This is particularly important as the use of AI continues to expand across various sectors.
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