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 4226–4250 of 4925 papers

TitleStatusHype
Non-Structured DNN Weight Pruning -- Is It Beneficial in Any Platform?—0
Don't take it lightly: Phasing optical random projections with unknown operatorsCode0
Deep Convolutional Compression for Massive MIMO CSI Feedback—0
Compression of Acoustic Event Detection Models With Quantized Distillation—0
Weight Normalization based Quantization for Deep Neural Network Compression—0
BTEL: A Binary Tree Encoding Approach for Visual Localization—0
Detection of small changes in medical and random-dot images comparing self-organizing map performance to human detection—0
Gridless Multisnapshot Variational Line Spectral Estimation from Coarsely Quantized Samples—0
Back to Simplicity: How to Train Accurate BNNs from Scratch?—0
Deep Learning-Based Quantization of L-Values for Gray-Coded ModulationCode0
Quantized Three-Ion-Channel Neuron Model for Neural Action Potentials—0
Deep Recurrent Quantization for Generating Sequential Binary CodesCode0
Beyond Product Quantization: Deep Progressive Quantization for Image RetrievalCode0
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks—0
Parameterized Structured Pruning for Deep Neural Networks—0
BasisConv: A method for compressed representation and learning in CNNs—0
Data-Free Quantization Through Weight Equalization and Bias CorrectionCode1
Table-Based Neural Units: Fully Quantizing Networks for Multiply-Free Inference—0
Fighting Quantization Bias With Bias—0
Deep Spherical Quantization for Image Search—0
Word-based Domain Adaptation for Neural Machine Translation—0
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations—0
Exploiting Offset-guided Network for Pose Estimation and Tracking—0
Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge IntelligenceCode0
Efficient 8-Bit Quantization of Transformer Neural Machine Language Translation Model—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