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 3276–3300 of 4925 papers

TitleStatusHype
Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost—0
FastSGD: A Fast Compressed SGD Framework for Distributed Machine Learning—0
Implicit Neural Representations for Image Compression—0
Neural Network Quantization for Efficient Inference: A Survey—0
A Generalized Zero-Shot Quantization of Deep Convolutional Neural Networks via Learned Weights Statistics—0
A comparison study of CNN denoisers on PRNU extraction—0
Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation—0
Equal Bits: Enforcing Equally Distributed Binary Network WeightsCode0
High-Resolution WiFi Imaging with Reconfigurable Intelligent Surfaces—0
Adaptive Proximal Gradient Methods for Structured Neural Networks—0
Exploration into Translation-Equivariant Image QuantizationCode0
Attribute Artifacts Removal for Geometry-based Point Cloud Compression—0
Hardware-friendly Deep Learning by Network Quantization and Binarization—0
Communication-Efficient Federated Learning via Quantized Compressed Sensing—0
A Highly Effective Low-Rank Compression of Deep Neural Networks with Modified Beam-Search and Modified Stable Rank—0
Mixed Precision of Quantization of Transformer Language Models for Speech Recognition—0
Mixed Precision Low-bit Quantization of Neural Network Language Models for Speech Recognition—0
Low-bit Quantization of Recurrent Neural Network Language Models Using Alternating Direction Methods of Multipliers—0
Enabling Fast Deep Learning on Tiny Energy-Harvesting IoT Devices—0
An Optimization Framework for Federated Edge Learning—0
QNNVerifier: A Tool for Verifying Neural Networks using SMT-Based Model Checking—0
A Novel Framework for Image-to-image Translation and Image Compression—0
Accelerating Deep Learning with Dynamic Data Pruning—0
HERO: Hessian-Enhanced Robust Optimization for Unifying and Improving Generalization and Quantization PerformanceCode0
Full-Duplex Massive MIMO Cellular Networks with Low Resolution ADC/DAC—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