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 4326–4350 of 4925 papers

TitleStatusHype
Scalable Methods for 8-bit Training of Neural NetworksCode0
Benchmarking Post-Training Quantization in LLMs: Comprehensive Taxonomy, Unified Evaluation, and Comparative AnalysisCode0
Quantitative Analysis of Performance Drop in DeepSeek Model QuantizationCode0
Megapixel Image Generation with Step-Unrolled Denoising AutoencodersCode0
Compressed 3D Gaussian Splatting for Accelerated Novel View SynthesisCode0
Trio-ViT: Post-Training Quantization and Acceleration for Softmax-Free Efficient Vision TransformerCode0
Fast Adjustable Threshold For Uniform Neural Network Quantization (Winning solution of LPIRC-II)Code0
FALCON: Feature-Label Constrained Graph Net Collapse for Memory Efficient GNNsCode0
Scalable Recognition with a Vocabulary TreeCode0
McQueen : Mixed Precision Quantization of Early Exit NetworksCode0
Make RepVGG Greater Again: A Quantization-aware ApproachCode0
Quantization and Deployment of Deep Neural Networks on MicrocontrollersCode0
Toward Accurate Post-Training Quantization for Image Super ResolutionCode0
Maestro: Uncovering Low-Rank Structures via Trainable DecompositionCode0
Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-AirCode0
FairGLVQ: Fairness in Partition-Based ClassificationCode0
Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector QuantizationCode0
Scalable Verification of Quantized Neural Networks (Technical Report)Code0
Natural Quantization of Neural NetworksCode0
LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical ImagesCode0
Structured Pruning and Quantization for Learned Image CompressionCode0
Unsupervised Person Re-identification via Softened Similarity LearningCode0
An Overview of Arithmetic Adaptations for Inference of Convolutional Neural Networks on Re-configurable HardwareCode0
LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model CompressionCode0
Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural NetworksCode0
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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