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 3476–3500 of 4925 papers

TitleStatusHype
LNS-Madam: Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update—0
PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation—0
Countering Adversarial Examples: Combining Input Transformation and Noisy Training—0
Preliminary study on using vector quantization latent spaces for TTS/VC systems with consistent performance—0
Quantization Aware Training, ERNIE and Kurtosis Regularizer: a short empirical study—0
Transform-Based Feature Map Compression for CNN Inference—0
Over-the-Air Computation via Cloud Radio Access Networks—0
On Minimizing Symbol Error Rate Over Fading Channels with Low-Resolution Quantization—0
Tensor Learning-based Precoder Codebooks for FD-MIMO Systems—0
Efficient Inference via Universal LSH Kernel—0
Witten-type topological field theory of self-organized criticality for stochastic neural networks—0
PVGNet: A Bottom-Up One-Stage 3D Object Detector With Integrated Multi-Level Features—0
Automated Log-Scale Quantization for Low-Cost Deep Neural Networks—0
QPP: Real-Time Quantization Parameter Prediction for Deep Neural Networks—0
Communication Efficient SGD via Gradient Sampling With Bayes Prior—0
Event-Based Bispectral Photometry Using Temporally Modulated Illumination—0
Deep Perceptual Preprocessing for Video Coding—0
Optimal Quantization Using Scaled Codebook—0
Distribution-Aware Adaptive Multi-Bit Quantization—0
Effects of VLSI Circuit Constraints on Temporal-Coding Multilayer Spiking Neural Networks—0
Light Lies: Optical Adversarial Attack—0
Quantized Federated Learning under Transmission Delay and Outage Constraints—0
Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional ComputingCode0
Unsupervised classification of cell imaging data using the quantization error in a Self Organizing Map—0
On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks—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