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 651–700 of 4925 papers

TitleStatusHype
Anchor-based Plain Net for Mobile Image Super-ResolutionCode1
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed TrainingCode1
BinaryDM: Accurate Weight Binarization for Efficient Diffusion ModelsCode1
Binary Latent DiffusionCode1
FP4 All the Way: Fully Quantized Training of LLMsCode1
Hierarchical Quantized AutoencodersCode1
EMQ: Evolving Training-free Proxies for Automated Mixed Precision QuantizationCode1
Enabling Binary Neural Network Training on the EdgeCode1
Binary TTC: A Temporal Geofence for Autonomous NavigationCode1
Improvements to Target-Based 3D LiDAR to Camera CalibrationCode1
Efficient-VDVAE: Less is moreCode1
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer ProgrammingCode1
Embedding in Recommender Systems: A SurveyCode1
End-to-End Rate-Distortion Optimized 3D Gaussian RepresentationCode1
Efficient and Robust Quantization-aware Training via Adaptive Coreset SelectionCode1
Efficient Quantized Sparse Matrix Operations on Tensor CoresCode1
End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video CompressionCode1
EFaR 2023: Efficient Face Recognition CompetitionCode1
IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion ModelsCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
EA-3DGS: Efficient and Adaptive 3D Gaussians with Highly Enhanced Quality for outdoor scenesCode1
EdgeQAT: Entropy and Distribution Guided Quantization-Aware Training for the Acceleration of Lightweight LLMs on the EdgeCode1
Dynamic Network Quantization for Efficient Video InferenceCode1
Join the High Accuracy Club on ImageNet with A Binary Neural Network TicketCode1
EasyQuant: Post-training Quantization via Scale OptimizationCode1
AdANNS: A Framework for Adaptive Semantic SearchCode1
Benchmarking of DL Libraries and Models on Mobile DevicesCode1
JointSQ: Joint Sparsification-Quantization for Distributed LearningCode1
BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial LearningCode1
Edge AI-Based Vein Detector for Efficient Venipuncture in the Antecubital FossaCode1
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion ModelsCode1
BitStack: Any-Size Compression of Large Language Models in Variable Memory EnvironmentsCode1
EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion ModelsCode1
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and QuantizationCode1
A Little Bit More: Bitplane-Wise Bit-Depth RecoveryCode1
LaCo: Large Language Model Pruning via Layer CollapseCode1
DQS3D: Densely-matched Quantization-aware Semi-supervised 3D DetectionCode1
Injecting Domain Adaptation with Learning-to-hash for Effective and Efficient Zero-shot Dense RetrievalCode1
Learnable Lookup Table for Neural Network QuantizationCode1
BL-JUNIPER: A CNN-Assisted Framework for Perceptual Video Coding Leveraging Block-Level JNDCode1
Adapting LLaMA Decoder to Vision TransformerCode1
Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts RemovalCode1
Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense RetrievalCode1
Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMsCode1
Catastrophic Failure of LLM Unlearning via QuantizationCode1
DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-chip TrainingCode1
Diverse Sample Generation: Pushing the Limit of Generative Data-free QuantizationCode1
Learning to Structure an Image with Few ColorsCode1
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical StudyCode1
Boosting Spike Camera Image Reconstruction from a Perspective of Dealing with Spike FluctuationsCode1
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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