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

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Showing 4401–4450 of 4925 papers

TitleStatusHype
Deep neural networks algorithms for stochastic control problems on finite horizon: convergence analysis—0
Proximal Mean-field for Neural Network QuantizationCode1
Trained Rank Pruning for Efficient Deep Neural NetworksCode1
DNQ: Dynamic Network Quantization—0
Prototype-based Neural Network Layers: Incorporating Vector Quantization—0
MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation—0
A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication—0
HitNet: Hybrid Ternary Recurrent Neural Network—0
Deep Signal Recovery with One-Bit Quantization—0
Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search—0
Quantity over Quality: Dithered Quantization for Compressive Radar Systems—0
Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependenceCode0
On Periodic Functions as Regularizers for Quantization of Neural Networks—0
Joint Neural Architecture Search and Quantization—0
Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation—0
HAQ: Hardware-Aware Automated Quantization with Mixed PrecisionCode2
QUENN: QUantization Engine for low-power Neural Networks—0
Iteratively Training Look-Up Tables for Network Quantization—0
Gaussian AutoEncoder—0
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training—0
Fast High-Dimensional Bilateral and Nonlocal Means FilteringCode0
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks—0
A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM—0
Deep Multiple Description Coding by Learning Scalar Quantization—0
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial AttacksCode0
One-Bit OFDM Receivers via Deep Learning—0
Online Embedding Compression for Text Classification using Low Rank Matrix Factorization—0
Towards Highly Accurate and Stable Face Alignment for High-Resolution VideosCode0
Rethinking floating point for deep learningCode0
Low-Precision Random Fourier Features for Memory-Constrained Kernel ApproximationCode0
Convolutional Neural Network Quantization using Generalized Gamma Distribution—0
Non-linear Canonical Correlation Analysis: A Compressed Representation Approach—0
DeepTwist: Learning Model Compression via Occasional Weight Distortion—0
A Novel Approach to Quantized Matrix Completion Using Huber Loss Measure—0
Low-complexity Recurrent Neural Network-based Polar Decoder with Weight Quantization Mechanism—0
Geometry and clustering with metrics derived from separable Bregman divergences—0
From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference—0
To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference—0
A Flexible, Extensible Software Framework for Neural Net Compression—0
Differentiable Training for Hardware Efficient LightNNs—0
Differentiable Fine-grained Quantization for Deep Neural Network CompressionCode0
Low-bit quantization and quantization-aware training for small-footprint keyword spotting—0
CNN inference acceleration using dictionary of centroids—0
Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction—0
Quantization for Rapid Deployment of Deep Neural Networks—0
Predictive Uncertainty through Quantization—0
A Novel Chaotic Uniform Quantizer for Speech Coding—0
Is PGD-Adversarial Training Necessary? Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples OnlyCode0
Rate Distortion For Model Compression: From Theory To Practice—0
Robust identification of thermal models for in-production High-Performance-Computing clusters with machine learning-based data selection—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