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 4701–4725 of 4925 papers

TitleStatusHype
Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask WeightsCode0
Activation Compression of Graph Neural Networks using Block-wise Quantization with Improved Variance MinimizationCode0
Decoupling Meta-Reinforcement Learning with Gaussian Task Contexts and SkillsCode0
A Simple Low-bit Quantization Framework for Video Snapshot Compressive ImagingCode0
Soft Weight-Sharing for Neural Network CompressionCode0
Regularized Classification-Aware QuantizationCode0
A Model for Every User and Budget: Label-Free and Personalized Mixed-Precision QuantizationCode0
Are You Getting What You Pay For? Auditing Model Substitution in LLM APIsCode0
Improving Self-Supervised Learning-based MOS Prediction NetworksCode0
David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep EdgeCode0
Channel-wise Mixed-precision Assignment for DNN Inference on Constrained Edge NodesCode0
Efficient Cross-Modal Retrieval via Deep Binary Hashing and QuantizationCode0
A Mixed Quantization Network for Computationally Efficient Mobile Inverse Tone MappingCode0
Playing Atari with Six NeuronsCode0
PQA: Exploring the Potential of Product Quantization in DNN Hardware AccelerationCode0
Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code MatchingCode0
PMQ-VE: Progressive Multi-Frame Quantization for Video EnhancementCode0
Improving Neural Network Quantization without Retraining using Outlier Channel SplittingCode0
Relaxed Quantization for Discretized Neural NetworksCode0
Training High-Performance and Large-Scale Deep Neural Networks with Full 8-bit IntegersCode0
Improved Knowledge Distillation for Crowd Counting on IoT DeviceCode0
Improved Gradient based Adversarial Attacks for Quantized NetworksCode0
Central Similarity Quantization for Efficient Image and Video RetrievalCode0
Implicit Feature Decoupling with Depthwise QuantizationCode0
Data Upcycling Knowledge Distillation for Image Super-ResolutionCode0
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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