AI RESEARCH
Toward Multi-Domain and Long-Tailed Quantization via Feature Alignment and Scaling
arXiv CS.LG
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ArXi:2606.04920v1 Announce Type: new 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), which aligns domain distributions through a CDF-based projection and uses sensitivity-aware weight aggregation to stabilize multi-domain quantization.