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 4626–4650 of 4925 papers

TitleStatusHype
Iterative Deep Compression : Compressing Deep Networks for Classification and Semantic Segmentation—0
Automated flow for compressing convolution neural networks for efficient edge-computation with FPGA—0
Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image—0
Data Clustering using a Hybrid of Fuzzy C-Means and Quantum-behaved Particle Swarm Optimization—0
Exploiting Modern Hardware for High-Dimensional Nearest Neighbor Search—0
AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training—0
Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition—0
Composite QuantizationCode0
Adaptive Quantization for Deep Neural Network—0
Triagem virtual de imagens de imuno-histoquímica usando redes neurais artificiais e espectro de padrões—0
Avaliação do método dialético na quantização de imagens multiespectrais—0
Fuzzy-Based Dialectical Non-Supervised Image Classification and Clustering—0
Simple strategies for recovering inner products from coarsely quantized random projections—0
Practical Data-Dependent Metric Compression with Provable Guarantees—0
Multiscale Quantization for Fast Similarity Search—0
Online Product Quantization—0
WSNet: Compact and Efficient Networks Through Weight Sampling—0
Expectation maximization transfer learning and its application for bionic hand prostheses—0
Warped-Linear Models for Time Series Classification—0
End-to-End Supervised Product Quantization for Image Search and Retrieval—0
Integral Human Pose RegressionCode0
Design of Sampling Set for Bandlimited Graph Signal Estimation—0
Deep reverse tone mappingCode0
qBitcoin: A Peer-to-Peer Quantum Cash System—0
A Genetic Algorithm Approach for ImageRepresentation Learning through Color Quantization—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