Selective Coupling of Decoupled Informative Regions: Masked Attention Alignment for Data-Free Quantization of Vision Transformers
arXiv:2606.04373v1 Announce Type: cross Abstract: Data-Free Quantization (DFQ) addresses data security concerns by synthesizing samples, without accessing real data. It has garnered increasing attention in the context of Vision Transformers (ViTs), owing to the superiority of the self-attention mechanism compared to classical convolutional operation. However, previous DFQ arts for ViTs often suffer from a distribution mismatch between synthetic samples and input distribution expected by quantized models Q, resulting in the suboptimal performance. In this paper, we propose a novel Masked Attent
The increasing focus on data privacy and security, particularly in sensitive applications of AI, makes data-free quantization a timely and important research area.
This research addresses a critical challenge in deploying efficient and secure AI models by enabling quantization without direct access to sensitive real-world data.
The proposed method could lead to more efficient and private AI model deployment for Vision Transformers, reducing the need for extensive real data during optimization.
- · Edge AI developers
- · Organizations with sensitive data
- · Vision Transformer deployments
- · Traditional data-intensive quantization methods
Improved efficiency and privacy for deployed Vision Transformer models.
Accelerated adoption of AI in privacy-sensitive sectors like healthcare and defense.
Potentially reduced compute requirements for AI training and deployment by optimizing model size without relying on large datasets.
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Read at arXiv cs.AI