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 3326–3350 of 4925 papers

TitleStatusHype
Automated Log-Scale Quantization for Low-Cost Deep Neural Networks—0
Optimal Quantization Using Scaled Codebook—0
Distribution-Aware Adaptive Multi-Bit Quantization—0
Deep Perceptual Preprocessing for Video Coding—0
PVGNet: A Bottom-Up One-Stage 3D Object Detector With Integrated Multi-Level Features—0
Effects of VLSI Circuit Constraints on Temporal-Coding Multilayer Spiking Neural Networks—0
Light Lies: Optical Adversarial Attack—0
VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice ConversionCode1
On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks—0
Unsupervised classification of cell imaging data using the quantization error in a Self Organizing Map—0
Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional ComputingCode0
Quantized Federated Learning under Transmission Delay and Outage Constraints—0
Development of Quantized DNN Library for Exact Hardware Emulation—0
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier IntegralsCode0
A White Paper on Neural Network Quantization—0
Compositional Sketch SearchCode0
FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing Applications—0
Energy-efficient Knowledge Distillation for Spiking Neural Networks—0
Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization—0
On the Convergence of Differentially Private Federated Learning on Non-Lipschitz Objectives, and with Normalized Client Updates—0
AI Enlightens Wireless Communication: Analyses, Solutions and Opportunities on CSI Feedback—0
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning—0
Cross-Modal Discrete Representation Learning—0
Fastening the Initial Access in 5G NR Sidelink for 6G V2X Networks—0
SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network—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