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 801–825 of 4925 papers

TitleStatusHype
Deep Transferring QuantizationCode1
Deep PeNSieve: A deep learning framework based on the posit number systemCode1
Deep Geometry Post-Processing for Decompressed Point CloudsCode1
QCM-SGM+: Improved Quantized Compressed Sensing With Score-Based Generative ModelsCode1
Deep Learning-Enabled One-Bit DoA EstimationCode1
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two QuantizationCode1
Mixed Precision DNNs: All you need is a good parametrizationCode1
Data-Free Quantization Through Weight Equalization and Bias CorrectionCode1
DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution NetworksCode1
QONNX: Representing Arbitrary-Precision Quantized Neural NetworksCode1
Data-Free Network Quantization With Adversarial Knowledge DistillationCode1
CNN-based first quantization estimation of double compressed JPEG imagesCode1
QSync: Quantization-Minimized Synchronous Distributed Training Across Hybrid DevicesCode1
QT-DoG: Quantization-aware Training for Domain GeneralizationCode1
Dataset Quantization with Active Learning based Adaptive SamplingCode1
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image TranslationCode1
QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit QuantizationCode1
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and QuantizationCode1
D^2-DPM: Dual Denoising for Quantized Diffusion Probabilistic ModelsCode1
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only InferenceCode1
Codebook Features: Sparse and Discrete Interpretability for Neural NetworksCode1
Unbiased Single-scale and Multi-scale Quantizers for Distributed OptimizationCode1
CPLLM: Clinical Prediction with Large Language ModelsCode1
CrAM: A Compression-Aware MinimizerCode1
DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector QuantizationCode1
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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