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 851–875 of 4925 papers

TitleStatusHype
Hierarchical Quantized AutoencodersCode1
Algorithm-hardware Co-design for Deformable ConvolutionCode1
Variational Bayesian QuantizationCode1
Robust Quantization: One Model to Rule Them AllCode1
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision ActivationsCode1
Learning Architectures for Binary NetworksCode1
Towards Sharper First-Order Adversary with Quantized GradientsCode1
Post-Training Piecewise Linear Quantization for Deep Neural NetworksCode1
Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded RepresentationsCode1
Least squares binary quantization of neural networksCode1
Fractional Skipping: Towards Finer-Grained Dynamic CNN InferenceCode1
Towards Accurate Post-training Network Quantization via Bit-Split and StitchingCode1
ZeroQ: A Novel Zero Shot Quantization FrameworkCode1
Towards Efficient Training for Neural Network QuantizationCode1
MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable QuantizationCode1
Online Learned Continual Compression with Adaptive Quantization ModulesCode1
Scientific Image Restoration AnywhereCode1
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural NetworksCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
ConveRT: Efficient and Accurate Conversational Representations from TransformersCode1
A holistic approach to polyphonic music transcription with neural networksCode1
Q8BERT: Quantized 8Bit BERTCode1
Improvements to Target-Based 3D LiDAR to Camera CalibrationCode1
And the Bit Goes Down: Revisiting the Quantization of Neural NetworksCode1
Data-Free Quantization Through Weight Equalization and Bias CorrectionCode1
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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