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 4101–4150 of 4925 papers

TitleStatusHype
DCNGAN: A Deformable Convolutional-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video—0
DC-PCN: Point Cloud Completion Network with Dual-Codebook Guided Quantization—0
Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers—0
Decentralized Low-Rank Fine-Tuning of Large Language Models—0
Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking—0
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression—0
Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval—0
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes—0
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning—0
DEED: A General Quantization Scheme for Communication Efficiency in Bits—0
Deep activity propagation via weight initialization in spiking neural networks—0
Deep and Shallow Covariance Feature Quantization for 3D Facial Expression Recognition—0
Deep Asymmetric Hashing with Dual Semantic Regression and Class Structure Quantization—0
Deep Attentive Generative Adversarial Network for Photo-Realistic Image De-Quantization—0
Deep Autoencoder-based Z-Interference Channels with Perfect and Imperfect CSI—0
Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes—0
Deep Conditional Measure Quantization—0
Deep Convolutional Compression for Massive MIMO CSI Feedback—0
Deep data compression for approximate ultrasonic image formation—0
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks—0
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization—0
Deep Fusion Model for Brain Tumor Classification Using Fine-Grained Gradient Preservation—0
DeepGEMM: Accelerated Ultra Low-Precision Inference on CPU Architectures using Lookup Tables—0
Deep Hashing With Minimal-Distance-Separated Hash Centers—0
Deep Hashing with Triplet Quantization Loss—0
Deep Hierarchy Quantization Compression algorithm based on Dynamic Sampling—0
DeepHQ: Learned Hierarchical Quantizer for Progressive Deep Image Coding—0
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing—0
Deep Imbalanced Regression via Hierarchical Classification Adjustment—0
Deep-Learned Compression for Radio-Frequency Signal Classification—0
Deep Learning-Aided Perturbation Model-Based Fiber Nonlinearity Compensation—0
Deep-Learning-Based Channel Estimation for Distributed MIMO with 1-bit Radio-Over-Fiber Fronthaul—0
Deep Learning based Fast and Accurate Beamforming for Millimeter-Wave Systems—0
Deep Learning-based Image Compression with Trellis Coded Quantization—0
Deep Learning-Based Intra Mode Derivation for Versatile Video Coding—0
Knowledge Distillation-aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems with Finite-Rate Feedback—0
Deep Learning-Based Real-Time Quality Control of Standard Video Compression for Live Streaming—0
Deep Learning for Distributed Optimization: Applications to Wireless Resource Management—0
Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems—0
Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G—0
Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit—0
Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks—0
Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems—0
Deep learning model compression using network sensitivity and gradients—0
Deep Learning to Ternary Hash Codes by Continuation—0
DeepliteRT: Computer Vision at the Edge—0
Deep Model Compression Via Two-Stage Deep Reinforcement Learning—0
Deep Multi-modality Soft-decoding of Very Low Bit-rate Face Videos—0
Deep Multiple Description Coding by Learning Scalar Quantization—0
Deep Neural Network-Based Quantized Signal Reconstruction for DOA Estimation—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