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 4251–4275 of 4925 papers

TitleStatusHype
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-OffCode0
SHE: A Fast and Accurate Deep Neural Network for Encrypted DataCode0
SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization—0
Fully Quantized Network for Object Detection—0
Learning Channel-Wise Interactions for Binary Convolutional Neural Networks—0
Compressing Unknown Images With Product Quantizer for Efficient Zero-Shot Classification—0
Deep Metric Learning to RankCode0
Enhanced Bayesian Compression via Deep Reinforcement Learning—0
Increasing Compactness Of Deep Learning Based Speech Enhancement Models With Parameter Pruning And Quantization Techniques—0
Deep Learning for Distributed Optimization: Applications to Wireless Resource Management—0
DeepShift: Towards Multiplication-Less Neural NetworksCode0
Quantization Loss Re-Learning Method—0
Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On MicrocontrollersCode0
Mixed Precision Training With 8-bit Floating Point—0
Instant Quantization of Neural Networks using Monte Carlo Methods—0
A Reconfigurable Dual-Mode Tracking SAR ADC without Analog Subtraction—0
Texture CNN for Thermoelectric Metal Pipe Image Classification—0
Brain-inspired reverse adversarial examples—0
Mixed Precision DNNs: All you need is a good parametrizationCode1
Learning In Practice: Reasoning About Quantization—0
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent—0
Quantization-Based Regularization for AutoencodersCode0
Natural Compression for Distributed Deep Learning—0
Communication-Efficient Distributed Blockwise Momentum SGD with Error-FeedbackCode0
HadaNets: Flexible Quantization Strategies for Neural Networks—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