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arXiv · 2606.04920

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

Ting-An Chen·Chin-Yuan Yeh·De-Nian Yang
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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), which aligns domain distributions through a CDF-based projection and uses sensitivity-aware weight aggregation to stabilize multi-domain quantization. We further extend EmaQ to EmaQ-LT for long-tailed quantization by introducing class-conditioned variance scaling and confidence-based logit adjustment to mitigate majority-class overconfidence. Theoretical analyses establish convergence guarantees and motivate the proposed sensitivity and scaling mechanisms. Experiments on standard, multi-domain (Office-31, Digits), and long-tailed (SynDigits-LT, CIFAR-10-LT, CIFAR-100-LT) benchmarks show that EmaQ and EmaQ-LT achieve strong low-bit performance under domain shift and class imbalance.

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Ting-An Chen, Chin-Yuan Yeh, De-Nian Yang. 2026-06-03. Toward Multi-Domain and Long-Tailed Quantization via Feature Alignment and Scaling. https://arxiv.org/abs/2606.04920

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