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

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Showing 576–600 of 4925 papers

TitleStatusHype
Low-Light Image Enhancement with Multi-Stage Residue Quantization and Brightness-Aware AttentionCode1
NAPA-VQ: Neighborhood-Aware Prototype Augmentation with Vector Quantization for Continual LearningCode1
ABCD: Arbitrary Bitwise Coefficient for De-QuantizationCode1
Guided Hybrid Quantization for Object detection in Multimodal Remote Sensing Imagery via One-to-one Self-teachingCode1
TeViS:Translating Text Synopses to Video StoryboardsCode1
FFNeRV: Flow-Guided Frame-Wise Neural Representations for VideosCode1
QuantArt: Quantizing Image Style Transfer Towards High Visual FidelityCode1
Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised LearningCode1
RepQ-ViT: Scale Reparameterization for Post-Training Quantization of Vision TransformersCode1
PD-Quant: Post-Training Quantization based on Prediction Difference MetricCode1
FretNet: Continuous-Valued Pitch Contour Streaming for Polyphonic Guitar Tablature TranscriptionCode1
NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision TransformersCode1
UDE: A Unified Driving Engine for Human Motion GenerationCode1
Post-training Quantization on Diffusion ModelsCode1
Accelerating Antimicrobial Peptide Discovery with Latent StructureCode1
Post-Processing Temporal Action DetectionCode1
Join the High Accuracy Club on ImageNet with A Binary Neural Network TicketCode1
Optimal Discrete Beamforming of RIS-Aided Wireless Communications: An Inner Product Maximization ApproachCode1
Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI FeedbackCode1
Quantized Compressed Sensing with Score-based Generative ModelsCode1
CNN-based first quantization estimation of double compressed JPEG imagesCode1
L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient and Accurate Deep LearningCode1
Machine Unlearning of Federated ClustersCode1
Too Brittle To Touch: Comparing the Stability of Quantization and Distillation Towards Developing Lightweight Low-Resource MT ModelsCode1
Broken Neural Scaling LawsCode1
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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