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

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Showing 4151–4200 of 4925 papers

TitleStatusHype
Recommending Pre-Trained Models for IoT Devices—0
Reconfigurable co-processor architecture with limited numerical precision to accelerate deep convolutional neural networks—0
Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer—0
Reconstruction-Computation-Quantization (RCQ): A Paradigm for Low Bit Width LDPC Decoding—0
Reconstruction Condition of Quantized Signals in Unlimited Sampling Framework—0
Recovery of sparse linear classifiers from mixture of responses—0
Recurrence of Optimum for Training Weight and Activation Quantized Networks—0
Recursive Quantization for L_2 Stabilization of a Finite Capacity Stochastic Control Loop with Intermittent State Observations—0
Redistribution of Weights and Activations for AdderNet Quantization—0
Reduced bit median quantization: A middle process for Efficient Image Compression—0
Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case—0
Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression—0
Reduced Reference Quality Assessment for Point Cloud Compression—0
Reducing Communication for Split Learning by Randomized Top-k Sparsification—0
Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation—0
InfLoR-SNN: Reducing Information Loss for Spiking Neural Networks—0
Reducing the Memory Footprint of 3D Gaussian Splatting—0
Reducing the Model Order of Deep Neural Networks Using Information Theory—0
Reducing the Side-Effects of Oscillations in Training of Quantized YOLO Networks—0
Reduplication across Categories in Cantonese—0
RefQSR: Reference-based Quantization for Image Super-Resolution Networks—0
Image restoration quality assessment based on regional differential information entropy—0
Region-of-Interest Based Neural Video Compression—0
Reg-PTQ: Regression-specialized Post-training Quantization for Fully Quantized Object Detector—0
Regularization-based Framework for Quantization-, Fault- and Variability-Aware Training—0
Regularization in Relevance Learning Vector Quantization Using l one Norms—0
Regularized Residual Quantization: a multi-layer sparse dictionary learning approach—0
Regularized Vector Quantization for Tokenized Image Synthesis—0
Reinforced Bit Allocation under Task-Driven Semantic Distortion Metrics—0
Reinforcement Learning for Finite Space Mean-Field Type Games—0
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence—0
Relative Entropy Regularized Reinforcement Learning for Efficient Encrypted Policy Synthesis—0
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks—0
Reliability-Aware Quantization for Anti-Aging NPUs—0
Reliability of PET/CT shape and heterogeneity features in functional and morphological components of Non-Small Cell Lung Cancer tumors: a repeatability analysis in a prospective multi-center cohort—0
Reliable edge machine learning hardware for scientific applications—0
ReLU and Addition-based Gated RNN—0
ReLU Neural Networks, Polyhedral Decompositions, and Persistent Homolog—0
Remember and Recall: Associative-Memory-based Trajectory Prediction—0
RepQ: Generalizing Quantization-Aware Training for Re-Parametrized Architectures—0
RepQuant: Towards Accurate Post-Training Quantization of Large Transformer Models via Scale Reparameterization—0
Representation Collapsing Problems in Vector Quantization—0
Representation Learning using Event-based STDP—0
Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain—0
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs—0
Rescuing Deep Hashing from Dead Bits Problem—0
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals—0
Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems—0
Resiliency of Deep Neural Networks under Quantization—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