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 4251–4300 of 4925 papers

TitleStatusHype
Compression of Acoustic Event Detection Models With Quantized Distillation—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
Beyond Product Quantization: Deep Progressive Quantization for Image RetrievalCode0
Deep Recurrent Quantization for Generating Sequential Binary CodesCode0
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks—0
Parameterized Structured Pruning for Deep Neural Networks—0
Table-Based Neural Units: Fully Quantizing Networks for Multiply-Free Inference—0
BasisConv: A method for compressed representation and learning in CNNs—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
Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge IntelligenceCode0
Exploiting Offset-guided Network for Pose Estimation and Tracking—0
Efficient 8-Bit Quantization of Transformer Neural Machine Language Translation Model—0
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-OffCode0
SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization—0
Learning Channel-Wise Interactions for Binary Convolutional Neural Networks—0
SHE: A Fast and Accurate Deep Neural Network for Encrypted DataCode0
Fully Quantized Network for Object Detection—0
Deep Metric Learning to RankCode0
Compressing Unknown Images With Product Quantizer for Efficient Zero-Shot Classification—0
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
Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On MicrocontrollersCode0
Quantization Loss Re-Learning Method—0
Mixed Precision Training With 8-bit Floating Point—0
Instant Quantization of Neural Networks using Monte Carlo Methods—0
Texture CNN for Thermoelectric Metal Pipe Image Classification—0
Brain-inspired reverse adversarial examples—0
A Reconfigurable Dual-Mode Tracking SAR ADC without Analog Subtraction—0
Quantization-Based Regularization for AutoencodersCode0
Natural Compression for Distributed Deep Learning—0
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent—0
Learning In Practice: Reasoning About Quantization—0
Communication-Efficient Distributed Blockwise Momentum SGD with Error-FeedbackCode0
HadaNets: Flexible Quantization Strategies for Neural Networks—0
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization—0
Additive Noise Annealing and Approximation Properties of Quantized Neural NetworksCode0
One-bit LFMCW Radar: Spectrum Analysis and Target Detection—0
NTP : A Neural Network Topology Profiler—0
Derived Codebooks for High-Accuracy Nearest Neighbor Search—0
Optimizing MRF-ASL Scan Design for Precise Quantification of Brain Hemodynamics using Neural Network Regression—0
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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