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 3376–3400 of 4925 papers

TitleStatusHype
Beyond Quantization: Power aware neural networks—0
Toward Efficient Low-Precision Training: Data Format Optimization and Hysteresis Quantization—0
Succinct Compression: Near-Optimal and Lossless Compression of Deep Neural Networks during Inference Runtime—0
Contrastive Mutual Information Maximization for Binary Neural Networks—0
Contrastive Quant: Quantization Makes Stronger Contrastive Learning—0
PIVQGAN: Posture and Identity Disentangled Image-to-Image Translation via Vector Quantization—0
CSQ: Centered Symmetric Quantization for Extremely Low Bit Neural Networks—0
Specialized Transformers: Faster, Smaller and more Accurate NLP Models—0
Post-Training Quantization Is All You Need to Perform Cross-Platform Learned Image Compression—0
Lattice Quantization—0
Delving into Channels: Exploring Hyperparameter Space of Channel Bit Widths with Linear Complexity—0
Differentiable Discrete Device-to-System Codesign for Optical Neural Networks via Gumbel-Softmax—0
Riemannian Manifold Embeddings for Straight-Through Estimator—0
Revisiting Locality-Sensitive Binary Codes from Random Fourier Features—0
Efficient Point Transformer for Large-scale 3D Scene Understanding—0
HoloFormer: Deep Compression of Pre-Trained Transforms via Unified Optimization of N:M Sparsity and Integer Quantization—0
Faster Neural Net Inference via Forests of Sparse Oblique Decision Trees—0
Wavelet Feature Maps Compression for Low Bandwidth Convolutional Neural Networks—0
Quantized sparse PCA for neural network weight compression—0
Full-Precision Free Binary Graph Neural Networks—0
Click-through Rate Prediction with Auto-Quantized Contrastive Learning—0
Performance Analysis of IRS-Assisted Cell-Free Communication—0
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation AnalysisCode0
Distribution-sensitive Information Retention for Accurate Binary Neural Network—0
Predicting Attention Sparsity in Transformers—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