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 3951–3975 of 4925 papers

TitleStatusHype
Probability Weighted Compact Feature for Domain Adaptive RetrievalCode1
Cluster Pruning: An Efficient Filter Pruning Method for Edge AI Vision Applications—0
Optimizing JPEG Quantization for Classification Networks—0
A Survey on Deep Hashing Methods—0
VQ-DRAW: A Sequential Discrete VAECode1
Phoenix: A Low-Precision Floating-Point Quantization Oriented Architecture for Convolutional Neural Networks—0
WaveQ: Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization—0
Image Hashing by Minimizing Discrete Component-wise Wasserstein DistanceCode0
Automatic Perturbation Analysis for Scalable Certified Robustness and BeyondCode1
Quantized Neural Network Inference with Precision Batching—0
Moniqua: Modulo Quantized Communication in Decentralized SGD—0
Generalized Product Quantization Network for Semi-supervised Image RetrievalCode1
Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of TransformersCode1
Adversarial Attack on Deep Product Quantization Network for Image Retrieval—0
Optimal Gradient Quantization Condition for Communication-Efficient Distributed Training—0
Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks—0
Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees—0
Searching for Winograd-aware Quantized NetworksCode1
OptComNet: Optimized Neural Networks for Low-Complexity Channel Estimation—0
Exploring the Connection Between Binary and Spiking Neural NetworksCode1
Revisiting Saliency Metrics: Farthest-Neighbor Area Under CurveCode0
Quantized Decentralized Stochastic Learning over Directed Graphs—0
PoET-BiN: Power Efficient Tiny Binary Neurons—0
New Bounds For Distributed Mean Estimation and Variance Reduction—0
Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems—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