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 3576–3600 of 4925 papers

TitleStatusHype
Soft then Hard: Rethinking the Quantization in Neural Image Compression—0
A Novel Unified Model for Multi-exposure Stereo Coding Based on Low Rank Tucker-ALS and 3D-HEVC—0
Q-matrix Unaware Double JPEG Detection using DCT-Domain Deep BiLSTM Network—0
Quantized State Feedback Stabilization of Nonlinear Systems under Denial-of-Service—0
Functional quantization of rough volatility and applications to volatility derivatives—0
Learned transform compression with optimized entropy encodingCode0
Towards On-Device Face Recognition in Body-worn Cameras—0
TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT—0
Binary Neural Network for Speaker Verification—0
Unconstrained Face Recognition using ASURF and Cloud-Forest Classifier optimized with VLAD—0
Arabic Compact Language Modelling for Resource Limited Devices—0
Bit-Mixer: Mixed-precision networks with runtime bit-width selection—0
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air—0
Zero-shot Adversarial Quantization—0
Automated Backend-Aware Post-Training Quantization—0
Scalable and Efficient Neural Speech Coding: A Hybrid Design—0
Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design—0
A Survey of Quantization Methods for Efficient Neural Network Inference—0
DNN Quantization with Attention—0
RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation—0
The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architectures—0
Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval—0
n-hot: Efficient bit-level sparsity for powers-of-two neural network quantization—0
Evaluating Post-Training Compression in GANs using Locality-Sensitive Hashing—0
Resilient Control under Quantization and Denial-of-Service: Co-designing a Deadbeat Controller and Transmission Protocol—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