Toward Multi-Domain and Long-Tailed Quantization via Feature Alignment and Scaling 事件

Toward Multi-Domain and Long-Tailed Quantization via Feature Alignment and Scaling arXiv:2606.04920v1 Announce Type: cross Abstract: Quantizing deep neural networks is essential for efficient inference on resource-constrained devices. However, most existing methods are designed for single-domain and class-balanced data, leaving practical settings with domain shifts or severe class imbalance underexplored. We address these challenges with Efficient Multi-Domain Alignment Quantization (EmaQ), whi