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 1176–1200 of 4925 papers

TitleStatusHype
Iterative Training: Finding Binary Weight Deep Neural Networks with Layer BinarizationCode0
Is PGD-Adversarial Training Necessary? Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples OnlyCode0
IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural NetworksCode0
Forward and Backward Information Retention for Accurate Binary Neural NetworksCode0
I&S-ViT: An Inclusive & Stable Method for Pushing the Limit of Post-Training ViTs QuantizationCode0
BitMoD: Bit-serial Mixture-of-Datatype LLM AccelerationCode0
Integral Human Pose RegressionCode0
Integer Quantization for Deep Learning Inference: Principles and Empirical EvaluationCode0
Integrated Encoding and Quantization to Enhance Quanvolutional Neural NetworksCode0
Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language ModelsCode0
FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going BeyondCode0
Instance-Aware Dynamic Neural Network QuantizationCode0
Improving Self-Supervised Learning-based MOS Prediction NetworksCode0
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence ModelsCode0
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision WeightsCode0
Improving Neural Network Quantization without Retraining using Outlier Channel SplittingCode0
AdaBits: Neural Network Quantization with Adaptive Bit-WidthsCode0
BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized WeightsCode0
An efficient and straightforward online quantization method for a data stream through remove-birth updatingCode0
AdaBin: Improving Binary Neural Networks with Adaptive Binary SetsCode0
An Edge Computing-Based Solution for Real-Time Leaf Disease Classification using Thermal ImagingCode0
Improved Gradient based Adversarial Attacks for Quantized NetworksCode0
Improved Knowledge Distillation for Crowd Counting on IoT DeviceCode0
Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code MatchingCode0
Image Hashing by Minimizing Discrete Component-wise Wasserstein DistanceCode0
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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