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 4676–4700 of 4925 papers

TitleStatusHype
DeepCABAC: A Universal Compression Algorithm for Deep Neural NetworksCode0
Integer Quantization for Deep Learning Inference: Principles and Empirical EvaluationCode0
Integer-Only Neural Network Quantization Scheme Based on Shift-Batch-NormalizationCode0
Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate RepresentationCode0
Instance-Aware Dynamic Neural Network QuantizationCode0
Perceptual Quality Assessment of Trisoup-Lifting Encoded 3D Point CloudsCode0
SNN4Agents: A Framework for Developing Energy-Efficient Embodied Spiking Neural Networks for Autonomous AgentsCode0
Fast Point Cloud Geometry Compression with Context-based Residual Coding and INR-based RefinementCode0
Reducing Inference Energy Consumption Using Dual Complementary CNNsCode0
A Transfer Learning and Explainable Solution to Detect mpox from Smartphones imagesCode0
Reducing Storage of Pretrained Neural Networks by Rate-Constrained Quantization and Entropy CodingCode0
Ternary Singular Value Decomposition as a Better Parameterized Form in Linear MappingCode0
Performance, Power, and Area Design Trade-offs in Millimeter-Wave Transmitter Beamforming ArchitecturesCode0
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision WeightsCode0
A Tale of Two Models: Constructing Evasive Attacks on Edge ModelsCode0
Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural NetworksCode0
A multimodal dynamical variational autoencoder for audiovisual speech representation learningCode0
Refining a k-nearest neighbor graph for a computationally efficient spectral clusteringCode0
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence ModelsCode0
Refining a -nearest neighbor graph for a computationally efficient spectral clusteringCode0
Physics-Conditioned Diffusion Models for Lattice Gauge TheoryCode0
Physics Inspired Criterion for Pruning-Quantization Joint LearningCode0
Deep attention-based classification network for robust depth predictionCode0
Picking Up Quantization Steps for Compressed Image ClassificationCode0
Soft Quantization using Entropic RegularizationCode0
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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