IEEE

I²NQ: Inter and Intra Nonuniform Quantization for Single Image Super-Resolution

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 ...
IEEE

Subset-Selection Weight Post-Training Quantization Method for Learned Image Compression Task

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 ...
GitHub

AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm Quantizer

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 ...
GitHub

Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey

2014_IEEE Multimedia_PRVQ_Projected residual vector quantization for ANN search. [IEEE] 2015_arXiv_IRVQ_Improved Residual Vector Quantization for High-dimensional Approximate Nearest Neighbor Search. ...