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 3701–3725 of 4925 papers

TitleStatusHype
BAMSProd: A Step towards Generalizing the Adaptive Optimization Methods to Deep Binary Model—0
Once Quantized for All: Progressively Searching for Quantized Compact Models—0
Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning ClassificationCode0
Rotated Binary Neural NetworkCode1
On Efficient Constructions of Checkpoints—0
TernaryBERT: Distillation-aware Ultra-low Bit BERTCode0
Learning to Improve Image Compression without Changing the Standard DecoderCode1
Perceptual Video Quality Prediction Emphasizing Chroma Distortions—0
Adaptive Debanding FilterCode1
A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback—0
Probabilistically Sampled and Spectrally Clustered Plant Genotypes using Phenotypic Characteristics—0
Searching for Low-Bit Weights in Quantized Neural NetworksCode1
Resource Allocation and Dithering of Bayesian Parameter Estimation Using Mixed-Resolution Data—0
Towards Fully 8-bit Integer Inference for the Transformer Model—0
Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation—0
MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework—0
Approximate spectral clustering using both reference vectors and topology of the network generated by growing neural gasCode0
Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts RemovalCode1
Fast Implementation of 4-bit Convolutional Neural Networks for Mobile Devices—0
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM—0
QuantNet: Learning to Quantize by Learning within Fully Differentiable Framework—0
On the Orthogonality of Knowledge Distillation with Other Techniques: From an Ensemble Perspective—0
FleXOR: Trainable Fractional Quantization—0
Alternating Direction Method of Multipliers for Quantization—0
ECG Beats Fast Classification Base on Sparse DictionariesCode0
Show:102550
← PrevPage 149 of 197Next →
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