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 3551–3575 of 4925 papers

TitleStatusHype
Computational data analysis for first quantization estimation on JPEG double compressed imagesCode0
Quantization optimized with respect to the Haar basis—0
Who's a Good Boy? Reinforcing Canine Behavior in Real-Time using Machine LearningCode0
Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks—0
I-BERT: Integer-only BERT QuantizationCode2
Improving Low-Precision Network Quantization via Bin Regularization—0
Uniformity in Heterogeneity: Diving Deep Into Count Interval Partition for Crowd CountingCode1
RangeDet: In Defense of Range View for LiDAR-Based 3D Object DetectionCode1
Product Quantizer Aware Inverted Index for Scalable Nearest Neighbor Search—0
Improving Neural Network Efficiency via Post-Training Quantization With Adaptive Floating-PointCode1
Practical Locally Private Federated Learning with Communication Efficiency—0
Explore the Potential of CNN Low Bit Training—0
Incremental few-shot learning via vector quantization in deep embedded space—0
Post-Training Weighted Quantization of Neural Networks for Language Models—0
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic—0
Multi-Prize Lottery Ticket Hypothesis: Finding Generalizable and Efficient Binary Subnetworks in a Randomly Weighted Neural Network—0
Uniform-Precision Neural Network Quantization via Neural Channel Expansion—0
TwinDNN: A Tale of Two Deep Neural Networks—0
Weights Having Stable Signs Are Important: Finding Primary Subnetworks and Kernels to Compress Binary Weight Networks—0
Improving the accuracy of neural networks in analog computing-in-memory systems by a generalized quantization method—0
End-to-end Quantized Training via Log-Barrier Extensions—0
WAVEQ: GRADIENT-BASED DEEP QUANTIZATION OF NEURAL NETWORKS THROUGH SINUSOIDAL REGULARIZATIONCode0
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization—0
Semi-Relaxed Quantization with DropBits: Training Low-Bit Neural Networks via Bitwise Regularization—0
Hybrid and Non-Uniform DNN quantization methods using Retro Synthesis data for efficient inference—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