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 3876–3900 of 4925 papers

TitleStatusHype
Byzantine-Resilient Secure Federated Learning—0
Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization—0
Differentiable Joint Pruning and Quantization for Hardware Efficiency—0
DBQ: A Differentiable Branch Quantizer for Lightweight Deep Neural Networks—0
Exploiting vulnerabilities of deep neural networks for privacy protectionCode0
Training with reduced precision of a support vector machine model for text classification—0
FSpiNN: An Optimization Framework for Memory- and Energy-Efficient Spiking Neural Networks—0
eSampling: Energy Harvesting ADCs—0
Compression strategies and space-conscious representations for deep neural networks—0
A General Family of Stochastic Proximal Gradient Methods for Deep Learning—0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Image De-Quantization Using Generative Models as Priors—0
Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning—0
Experimental results on palmvein-based personal recognition by multi-snapshot fusion of textural features—0
Term Revealing: Furthering Quantization at Run Time on Quantized DNNs—0
Quantization in Relative Gradient Angle Domain For Building Polygon Estimation—0
SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization—0
AUSN: Approximately Uniform Quantization by Adaptively Superimposing Non-uniform Distribution for Deep Neural Networks—0
Enabling On-Device CNN Training by Self-Supervised Instance Filtering and Error Map Pruning—0
Learning with tree tensor networks: complexity estimates and model selection—0
Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights—0
Compressing Neural Machine Translation Models with 4-bit Precision—0
Edinburgh's Submissions to the 2020 Machine Translation Efficiency Task—0
Learning to Quantize Deep Neural Networks: A Competitive-Collaborative Approach—0
Robust Product Markovian Quantization—0
Show:102550
← PrevPage 156 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