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 4851–4875 of 4925 papers

TitleStatusHype
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks—0
Some Further Evidence about Magnification and Shape in Neural Gas—0
Quantized Nonparametric Estimation over Sobolev Ellipsoids—0
Variational Optimization of Annealing Schedules—0
Vector Quantization by Minimizing Kullback-Leibler Divergence—0
Pairwise Rotation Hashing for High-dimensional Features—0
Face recognition using color local binary pattern from mutually independent color channels—0
Compressing Deep Convolutional Networks using Vector Quantization—0
Background Modelling using Octree Color Quantization—0
Hashing on Nonlinear Manifolds—0
Self-Adaptable Templates for Feature Coding—0
Quantized Kernel Learning for Feature Matching—0
Speaker Identification From Youtube Obtained Data—0
Stacked Quantizers for Compositional Vector CompressionCode0
Efficient multivariate sequence classification—0
Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification—0
Seeing through bag-of-visual-word glasses: towards understanding quantization effects in feature extraction methods—0
Lightweight Client-Side Chinese/Japanese Morphological Analyzer Based on Online LearningCode0
Object Proposal Generation using Two-Stage Cascade SVMs—0
Efficient On-the-fly Category Retrieval using ConvNets and GPUs—0
User Modeling by Using Bag-of-Behaviors for Building a Dialog System Sensitive to the Interlocutor's Internal State—0
Locally Linear Hashing for Extracting Non-Linear Manifolds—0
Distance Encoded Product Quantization—0
Compact Representation for Image Classification: To Choose or to Compress?—0
Learning Receptive Fields for Pooling from Tensors of Feature Response—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