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 1726–1750 of 4925 papers

TitleStatusHype
A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization—0
Accuracy is Not All You Need—0
Compression-based Privacy Preservation for Distributed Nash Equilibrium Seeking in Aggregative Games—0
A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks—0
Compressing Weight-updates for Image Artifacts Removal Neural Networks—0
A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning—0
Adversarial Defenses via Vector Quantization—0
Compressing VAE-Based Out-of-Distribution Detectors for Embedded Deployment—0
ASER: Activation Smoothing and Error Reconstruction for Large Language Model Quantization—0
Compressing Unknown Images With Product Quantizer for Efficient Zero-Shot Classification—0
Compressing Recurrent Neural Networks for FPGA-accelerated Implementation in Fluorescence Lifetime Imaging—0
A Secure Federated Learning Framework for Residential Short Term Load Forecasting—0
Adversarial Attack on Deep Product Quantization Network for Image Retrieval—0
Accumulator-Aware Post-Training Quantization—0
3D Surface Detail Enhancement From a Single Normal Map—0
Compressing Pre-trained Transformers via Low-Bit NxM Sparsity for Natural Language Understanding—0
Compressing Neural Machine Translation Models with 4-bit Precision—0
A-SDM: Accelerating Stable Diffusion through Redundancy Removal and Performance Optimization—0
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks—0
A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model—0
Compressing Language Models for Specialized Domains—0
Compressing Deep Convolutional Networks using Vector Quantization—0
Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden—0
Art and Science of Quantizing Large-Scale Models: A Comprehensive Overview—0
Compressed Video Super-Resolution based on Hierarchical Encoding—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