Abstract: Quantizing neural network is an efficient model compression technique that converts weights and activations from floating-point to integer. However, existing model quantization methods are ...
Abstract: Post-training quantization(PTQ) has been widely studied in recent years because it does not require retraining the network or the entire training dataset. However, naively applying the PTQ ...
This repository contains the official PyTorch implementation for the ECCV2024 paper "AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm Quantizer". AdaLog adapts the ...
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