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 3201–3250 of 4925 papers

TitleStatusHype
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
Vector Quantization Methods for Access Point Placement in Cell-Free Massive MIMO Systems—0
Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression—0
Vector Quantization With Self-Attention for Quality-Independent Representation Learning—0
G2P-DDM: Generating Sign Pose Sequence from Gloss Sequence with Discrete Diffusion Model—0
Show:102550
← PrevPage 65 of 99Next →
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