DUCKQUANT / THE QUANT & AI DIRECTORY
14,444 resources, ranked by in-directory PageRank
Showing 4,573–4,608 of 14,444 resources
Publication: BiDet: An Efficient Binarized Object Detector. Published Mar 9, 2020.
Publication: AQD: Towards Accurate Fully-Quantized Object Detection. Published Jul 14, 2020.
Publication: Improving Post-Training Quantization on Object Detection with Task Loss-Guided Lp Metric. Published Apr 19, 2023.
Publication: PACT: Parameterized Clipping Activation for Quantized Neural Networks. Published May 16, 2018.
Publication: Data-Free Quantization Through Weight Equalization and Bias Correction. Published 2019.
Publication: Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision. Published Feb 20, 2020.
Publication: Scalable Verification of Quantized Neural Networks (Technical Report). Published Dec 15, 2020.
This is the official pytorch implementation for paper: BiDet: An Efficient Binarized Object Detector, which is accepted by CVPR2020.
[CVPR 2019, Oral] HAQ: Hardware-Aware Automated Quantization with Mixed Precision
[CVPR'20] ZeroQ: A Novel Zero Shot Quantization Framework
Publication: ZeroQ: A Novel Zero Shot Quantization Framework. Published 2020.
Public repository by mlech26l. Primary language: Python.
Publication: Reg-PTQ: Regression-specialized Post-training Quantization for Fully Quantized Object Detector. Published 2024.
Publication: LSQ+: Improving Low-Bit Quantization Through Learnable Offsets and Better Initialization. Published 2020.
Publication: HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision. Published 2019.
Explore this resource at its source website for documentation and current information.
Publication: Matching-oriented Product Quantization For Ad-hoc Retrieval. Published Apr 16, 2021.
Publication: Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysis. Published Mar 27, 2023.
Publication: Fully Quantized Image Super-Resolution Networks. Published Nov 29, 2020.
Publication: DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks. Published 2022.
Publication: Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks. Published Mar 8, 2022.
Publication: SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings. Published Mar 22, 2023.
Public repository by microsoft. Primary language: Python.
Public repository by dooseokjeong. Primary language: Python.
A curated collection of papers, benchmarks, surveys, and tools for model quantization, covering low-bit networks, LLMs, multimodal and generative models, vector and lattice quantization,…
This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.
Codes for ACMMM 2021 paper "Fully Quantized Image Super-Resolution Networks".
[WACV2022] Official Code for the "DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks"
Pytorch implementation of our paper accepted by ECCV2022 -- Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks
[ECCV2022] Official Code for the "CADyQ: Content-Aware Dynamic Quantization for Image Super Resolution"
Energy efficiency is critical for running computer vision on battery-powered systems, such as mobile phones or UAVs (unmanned aerial vehicles, or drones). This
Embedding Quantization (Compress Word Embeddings)
[NeurIPS 2021] Source code for the paper "Qu-ANTI-zation: Exploiting Neural Network Quantization for Achieving Adversarial Outcomes"
