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 4551–4600 of 4925 papers

TitleStatusHype
Information and Statistical Efficiency When Quantizing Noisy DC Values—0
Dynamic Signal Measurements Based on Quantized Data—0
Link and code: Fast indexing with graphs and compact regression codesCode4
Quantized Compressive K-Means—0
Deep Convolutional AutoEncoder-based Lossy Image CompressionCode0
Robust Anomaly-Based Ship Proposals Detection from Pan-sharpened High-Resolution Satellite Image—0
Single-User mmWave Massive MIMO: SVD-based ADC Bit Allocation and Combiner Design—0
MQGrad: Reinforcement Learning of Gradient Quantization in Parameter Server—0
A Rigorous Analysis of Least Squares Sine Fitting Using Quantized Data: the Random Phase Case—0
On the Impact of Fixed Point Hardware for Optical Fiber Nonlinearity Compensation Algorithms—0
Value-aware Quantization for Training and Inference of Neural Networks—0
Efficient Channel Estimator with Angle-Division Multiple Access—0
Vision Based Dynamic Offside Line Marker for Soccer GamesCode1
Pieces of Eight: 8-bit Neural Machine Translation—0
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and MemoryCode0
Discriminative Cross-View Binary Representation Learning—0
A Compressed Sensing Approach for Distribution Matching—0
Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the YearsCode0
Bernoulli Embeddings for Graphs—0
Gradient Descent Quantizes ReLU Network Features—0
A Quantization-Friendly Separable Convolution for MobileNetsCode0
Incremental Color Quantization for Color-Vision-Deficient Observers Using Mobile Gaming Data—0
Unsupervised Semantic Deep Hashing—0
Word2Bits - Quantized Word VectorsCode0
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial ExamplesCode0
Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation—0
High-Accuracy Low-Precision TrainingCode0
Deep Neural Network Compression with Single and Multiple Level QuantizationCode0
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range ContentCode0
XNORBIN: A 95 TOp/s/W Hardware Accelerator for Binary Convolutional Neural Networks—0
Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination—0
Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine—0
Scalar Quantization as Sparse Least Square Optimization—0
L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks—0
Autoencoder based image compression: can the learning be quantization independent?—0
Loss-aware Weight Quantization of Deep NetworksCode0
3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning—0
Low complexity convolutional neural network for vessel segmentation in portable retinal diagnostic devices—0
Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation—0
Simultaneous Compression and Quantization: A Joint Approach for Efficient Unsupervised Hashing—0
RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural NetworkCode0
Model compression via distillation and quantizationCode0
Cell growth rate dictates the onset of glass to fluid-like transition and long time super-diffusion in an evolving cell colony—0
Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications—0
Persistence Codebooks for Topological Data Analysis—0
On the Needs for Rotations in Hypercubic Quantization Hashing—0
On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks—0
Topologically Controlled Lossy Compression—0
Universal Deep Neural Network Compression—0
Effective Quantization Approaches for Recurrent 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