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 4276–4300 of 4925 papers

TitleStatusHype
Additive Noise Annealing and Approximation Properties of Quantized Neural NetworksCode0
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization—0
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
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression—0
Optimizing MRF-ASL Scan Design for Precise Quantification of Brain Hemodynamics using Neural Network Regression—0
Learning to Groove with Inverse Sequence TransformationsCode1
MobiVSR: A Visual Speech Recognition Solution for Mobile Devices—0
Compressing Weight-updates for Image Artifacts Removal Neural Networks—0
Two-layer Near-lossless HDR Coding with Backward Compatibility to JPEG—0
Reconstruction of Privacy-Sensitive Data from Protected Templates—0
2-bit Model Compression of Deep Convolutional Neural Network on ASIC Engine for Image Retrieval—0
SinReQ: Generalized Sinusoidal Regularization for Low-Bitwidth Deep Quantized Training—0
Efficient Discrete Supervised Hashing for Large-scale Cross-modal Retrieval—0
Compression of Acoustic Event Detection Models with Low-rank Matrix Factorization and Quantization Training—0
Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM—0
Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network—0
Nested Dithered Quantization for Communication Reduction in Distributed Training—0
Dimension-Free Bounds for Low-Precision Training—0
Analysis of Quantized Models—0
ACIQ: Analytical Clipping for Integer Quantization of neural networksCode0
N-Ary Quantization for CNN Model Compression and Inference Acceleration—0
Adaptive Sample-space & Adaptive Probability coding: a neural-network based approach for compression—0
Unsupervised automatic classification of Scanning Electron Microscopy (SEM) images of CD4+ cells with varying extent of HIV virion infection—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