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 4676–4700 of 4925 papers

TitleStatusHype
Fuzzy Norm-Explicit Product Quantization for Recommender Systems—0
FxP-QNet: A Post-Training Quantizer for the Design of Mixed Low-Precision DNNs with Dynamic Fixed-Point Representation—0
GA-GAN: CT reconstruction from Biplanar DRRs using GAN with Guided Attention—0
GALA: Geometry-Aware Local Adaptive Grids for Detailed 3D Generation—0
Galaxy classification: A machine learning analysis of GAMA catalogue data—0
GANCompress: GAN-Enhanced Neural Image Compression with Binary Spherical Quantization—0
GAQAT: gradient-adaptive quantization-aware training for domain generalization—0
Gaussian Approximation of Quantization Error for Estimation from Compressed Data—0
Gaussian AutoEncoder—0
Gaussian Mixture Vector Quantization with Aggregated Categorical Posterior—0
Gaussian Rate-Distortion-Perception Coding and Entropy-Constrained Scalar Quantization—0
GDRQ: Group-based Distribution Reshaping for Quantization—0
GEB-1.3B: Open Lightweight Large Language Model—0
GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning—0
Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translation—0
Generalization Error Analysis of Quantized Compressive Learning—0
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles—0
Line Spectrum Estimation and Detection with Few-bit ADCs: Theoretical Analysis and Generalized NOMP Algorithm—0
Generalized residual vector quantization for large scale data—0
General Point Model with Autoencoding and Autoregressive—0
General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference—0
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks—0
Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior—0
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—0
Generative Design of Hardware-aware DNNs—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