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 2701–2725 of 4925 papers

TitleStatusHype
Low Rank Optimization for Efficient Deep Learning: Making A Balance between Compact Architecture and Fast Training—0
Fighting over-fitting with quantization for learning deep neural networks on noisy labels—0
Quantized Zero Dynamics Attacks against Sampled-data Control Systems—0
R2 Loss: Range Restriction Loss for Model Compression and Quantization—0
Rediscovering Hashed Random Projections for Efficient Quantization of Contextualized Sentence EmbeddingsCode0
Bag of Tricks with Quantized Convolutional Neural Networks for image classification—0
Modular Quantization-Aware Training for 6D Object Pose EstimationCode0
Regularized Vector Quantization for Tokenized Image Synthesis—0
Entropy Coding Improvement for Low-complexity Compressive Auto-encoders—0
Greener yet Powerful: Taming Large Code Generation Models with Quantization—0
Dynamic Stashing Quantization for Efficient Transformer Training—0
QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms—0
A Privacy Preserving System for Movie Recommendations Using Federated Learning—0
ML Codebook Design for Initial Access and CSI Type-II Feedback in Sub-6GHz 5G NR—0
MetaGrad: Adaptive Gradient Quantization with Hypernetworks—0
Fixed-point quantization aware training for on-device keyword-spotting—0
Summary Statistic Privacy in Data SharingCode0
Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors—0
Rotation Invariant Quantization for Model CompressionCode0
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages—0
Ternary Quantization: A Survey—0
Boosting Distributed Full-graph GNN Training with Asynchronous One-bit Communication—0
Comprehensive Comparisons of Uniform Quantization in Deep Image CompressionCode0
A Probabilistic Reformulation Technique for Discrete RIS Optimization in Wireless Systems—0
Ultra-low Precision Multiplication-free Training for Deep Neural Networks—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