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 3351–3400 of 4925 papers

TitleStatusHype
Unleashing Dynamic Range and Resolution in Unlimited Sensing Framework via Novel Hardware—0
Unlimited Sampling Radar: a Real-Time End-to-End Demonstrator—0
Unlocking Efficient Large Inference Models: One-Bit Unrolling Tips the Scales—0
Enhancing Multimodal Unified Representations for Cross Modal Generalization—0
Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models—0
Attention network forecasts time-to-failure in laboratory shear experiments—0
Unsupervised classification of cell imaging data using the quantization error in a Self Organizing Map—0
Grounded Object Centric Learning—0
Unsupervised Facial Performance Editing via Vector-Quantized StyleGAN Representations—0
Unsupervised Learning For Sequence-to-sequence Text-to-speech For Low-resource Languages—0
Unsupervised Multi-Criteria Adversarial Detection in Deep Image Retrieval—0
Unsupervised Quantized Prosody Representation for Controllable Speech Synthesis—0
Unsupervised Semantic Deep Hashing—0
Unsupervised Triplet Hashing for Fast Image Retrieval—0
UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles—0
Uplink Achievable Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave Systems with Resolution-Adaptive ADCs—0
Uplink Achievable Rate of Intelligent Reflecting Surface-Aided Millimeter-Wave Communications with Low-Resolution ADC and Phase Noise—0
Uplink Transmit Power Optimization for Distributed Massive MIMO Systems with 1-Bit ADCs—0
Up or Down? Adaptive Rounding for Post-Training Quantization—0
User Modeling by Using Bag-of-Behaviors for Building a Dialog System Sensitive to the Interlocutor's Internal State—0
Using dynamical quantization to perform split attempts in online tree regressors—0
Using Random Codebooks for Audio Neural AutoEncoders—0
Using Self-Organizing Maps for Sentiment Analysis—0
Using the quantization error from Self-Organized Map (SOM) output for detecting critical variability in large bodies of image time series in less than a minute—0
USM-Lite: Quantization and Sparsity Aware Fine-tuning for Speech Recognition with Universal Speech Models—0
USM RNN-T model weights binarization—0
Utilizing Explainable AI for Quantization and Pruning of Deep Neural Networks—0
UWC: Unit-wise Calibration Towards Rapid Network Compression—0
UW-CVGAN: UnderWater Image Enhancement with Capsules Vectors Quantization—0
VALL-E R: Robust and Efficient Zero-Shot Text-to-Speech Synthesis via Monotonic Alignment—0
Value-aware Quantization for Training and Inference of Neural Networks—0
Value-Driven Mixed-Precision Quantization for Patch-Based Inference on Microcontrollers—0
VAQF: Fully Automatic Software-Hardware Co-Design Framework for Low-Bit Vision Transformer—0
Variability-Aware Training and Self-Tuning of Highly Quantized DNNs for Analog PIM—0
Variable Bitrate Residual Vector Quantization for Audio Coding—0
Variable Rate Deep Image Compression With a Conditional Autoencoder—0
Variable-Rate Learned Image Compression with Multi-Objective Optimization and Quantization-Reconstruction Offsets—0
Variable Resolution Pixel Quantization for Low Power Machine Vision Application on Edge—0
Variational autoencoder-based neural network model compression—0
Variational Bayesian Channel Estimation and Data Detection for Cell-Free Massive MIMO with Low-Resolution Quantized Fronthaul Links—0
Variational Network Quantization—0
Variational Optimization of Annealing Schedules—0
Variation-aware Binarized Memristive Networks—0
VaSAB: The variable size adaptive information bottleneck for disentanglement on speech and singing voice—0
Vau da muntanialas: Energy-efficient multi-die scalable acceleration of RNN inference—0
VCVTS: Multi-speaker Video-to-Speech synthesis via cross-modal knowledge transfer from voice conversion—0
Scalar Quantization as Sparse Least Square Optimization—0
Vector Quantization by Minimizing Kullback-Leibler Divergence—0
Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO Systems—0
Vector Quantization for Machine Vision—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