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 4426–4450 of 4925 papers

TitleStatusHype
Joint Neural Architecture Search and Quantization—0
Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation—0
QUENN: QUantization Engine for low-power Neural Networks—0
Iteratively Training Look-Up Tables for Network Quantization—0
Gaussian AutoEncoder—0
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training—0
Fast High-Dimensional Bilateral and Nonlocal Means FilteringCode0
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks—0
Deep Multiple Description Coding by Learning Scalar Quantization—0
A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM—0
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial AttacksCode0
One-Bit OFDM Receivers via Deep Learning—0
Rethinking floating point for deep learningCode0
Online Embedding Compression for Text Classification using Low Rank Matrix Factorization—0
Towards Highly Accurate and Stable Face Alignment for High-Resolution VideosCode0
Convolutional Neural Network Quantization using Generalized Gamma Distribution—0
Non-linear Canonical Correlation Analysis: A Compressed Representation Approach—0
Low-Precision Random Fourier Features for Memory-Constrained Kernel ApproximationCode0
DeepTwist: Learning Model Compression via Occasional Weight Distortion—0
Low-complexity Recurrent Neural Network-based Polar Decoder with Weight Quantization Mechanism—0
A Novel Approach to Quantized Matrix Completion Using Huber Loss Measure—0
Geometry and clustering with metrics derived from separable Bregman divergences—0
From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference—0
To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference—0
Differentiable Fine-grained Quantization for Deep Neural Network CompressionCode0
Show:102550
← PrevPage 178 of 197Next →
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