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Q-OATS: Quantization with outlier-aware tensor-wise smoothing for CNNs
Model compression is crucial for optimizing Convolutional Neural Networks (CNNs) for deployment on resource-constrained devices. This paper introduces Quantization with Outlier-Aware Tensor-Wise Smoothing (Q-OATS), a novel technique that integrates outlier handling with traditional model compression
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