SOTAVerified

Quantization

Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).

Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4851–4900 of 4925 papers

TitleStatusHype
Hybrid Scene Compression for Visual Localization—0
Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression—0
Hybrid Weight Representation: A Quantization Method Represented with Ternary and Sparse-Large Weights—0
Hyperbolic Residual Quantization: Discrete Representations for Data with Latent Hierarchies—0
HyperDepth: Learning Depth From Structured Light Without Matching—0
Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine—0
Hyperpoints and Fine Vocabularies for Large-Scale Location Recognition—0
Hyperspectral recovery from RGB images using Gaussian Processes—0
Hyperspherical Loss-Aware Ternary Quantization—0
Hyperspherical Quantization: Toward Smaller and More Accurate Models—0
Hyperstroke: A Novel High-quality Stroke Representation for Assistive Artistic Drawing—0
HyperVQ: MLR-based Vector Quantization in Hyperbolic Space—0
ICQ: A Quantization Scheme for Best-Arm Identification Over Bit-Constrained Channels—0
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression—0
IDKM: Memory Efficient Neural Network Quantization via Implicit, Differentiable k-Means—0
IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision—0
I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models—0
Illumination and Temperature-Aware Multispectral Networks for Edge-Computing-Enabled Pedestrian Detection—0
ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA—0
Image Compression Based on Compressive Sensing: End-to-End Comparison with JPEG—0
Image Compression using only Attention based Neural Networks—0
Image Compression with Product Quantized Masked Image Modeling—0
Image De-Quantization Using Generative Models as Priors—0
Image processing in DNA—0
Image Shadow Removal Using End-to-End Deep Convolutional Neural Networks—0
Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering—0
Image Storage on Synthetic DNA Using Autoencoders—0
Image storage on synthetic DNA using compressive autoencoders and DNA-adapted entropy coders—0
IM-Loss: Information Maximization Loss for Spiking Neural Networks—0
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks—0
Impact of ML Optimization Tactics on Greener Pre-Trained ML Models—0
Implementation and Evaluation of Physical Layer Key Generation on SDR based LoRa Platform—0
Implementation of a framework for deploying AI inference engines in FPGAs—0
Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction—0
Implicit Neural Representations for Image Compression—0
Improved Convergence Rate for a Distributed Two-Time-Scale Gradient Method under Random Quantization—0
Improved Quantization Strategies for Managing Heavy-tailed Gradients in Distributed Learning—0
Improved Residual Vector Quantization for High-dimensional Approximate Nearest Neighbor Search—0
Improved training of binary networks for human pose estimation and image recognition—0
Improving Acoustic Scene Classification in Low-Resource Conditions—0
Improving Adversarial Robustness in Weight-quantized Neural Networks—0
Improving Approximate Optimal Transport Distances using Quantization—0
Improving Autoregressive Image Generation through Coarse-to-Fine Token Prediction—0
Improving Bilayer Product Quantization for Billion-Scale Approximate Nearest Neighbors in High Dimensions—0
Improving Block-Wise LLM Quantization by 4-bit Block-Wise Optimal Float (BOF4): Analysis and Variations—0
Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment—0
Improving K-Nearest Neighbor Efficacy for Farsi Text Classification—0
Improving Low-Precision Network Quantization via Bin Regularization—0
Improving LPCNet-based Text-to-Speech with Linear Prediction-structured Mixture Density Network—0
Improving Multi-generation Robustness of Learned Image Compression—0
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All datasetsImageNetCIFAR-10Wiki-40BAgeDB-30CFP-FPCOCO (Common Objects in Context)IJB-BIJB-CKnowledge-based:LFW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03—Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31—Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97—Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71—Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2—Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51—Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17—Unverified
8Xception W8A8Top-1 Accuracy (%)78.97—Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88—Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04—Unverified
2DTQMAP0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3—Unverified
2OutEffHop-Bert_basePerplexity6.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92—Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8—Unverified