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 3026–3050 of 4925 papers

TitleStatusHype
Channel Balancing for Accurate Quantization of Winograd Convolutions—0
Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code MatchingCode0
Instance-Aware Dynamic Neural Network QuantizationCode0
Data-Free Network Compression via Parametric Non-Uniform Mixed Precision Quantization—0
Learnable Lookup Table for Neural Network QuantizationCode1
Mutual Quantization for Cross-Modal Search With Noisy Labels—0
AlignQ: Alignment Quantization With ADMM-Based Correlation PreservationCode1
Mr.BiQ: Post-Training Non-Uniform Quantization Based on Minimizing the Reconstruction Error—0
RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks—0
SceneSqueezer: Learning To Compress Scene for Camera Relocalization—0
ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language GenerationCode1
Croesus: Multi-Stage Processing and Transactions for Video-Analytics in Edge-Cloud Systems—0
Studying the Interplay between Information Loss and Operation Loss in Representations for Classification—0
Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural NetworksCode1
Automatic Mixed-Precision Quantization Search of BERT—0
End-to-End Autoencoder Communications with Optimized Interference Suppression—0
HiKonv: High Throughput Quantized Convolution With Novel Bit-wise Management and Computation—0
Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reduction—0
Learning Cross-Scale Weighted Prediction for Efficient Neural Video CompressionCode1
BMPQ: Bit-Gradient Sensitivity Driven Mixed-Precision Quantization of DNNs from Scratch—0
Stochastic Learning Equation using Monotone Increasing Resolution of Quantization—0
Training Quantized Deep Neural Networks via Cooperative CoevolutionCode1
Distilling the Knowledge of Romanian BERTs Using Multiple TeachersCode0
Manifold learning via quantum dynamics—0
Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats—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