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 2526–2550 of 4925 papers

TitleStatusHype
QUENN: QUantization Engine for low-power Neural Networks—0
Query-Adaptive Hash Code Ranking for Large-Scale Multi-View Visual Search—0
QUIC-FL: Quick Unbiased Compression for Federated Learning—0
QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms—0
QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration—0
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning—0
QuPeL: Quantized Personalization with Applications to Federated Learning—0
QVD: Post-training Quantization for Video Diffusion Models—0
Qwen2.5-32B: Leveraging Self-Consistent Tool-Integrated Reasoning for Bengali Mathematical Olympiad Problem Solving—0
Q-YOLO: Efficient Inference for Real-time Object Detection—0
Q-YOLOP: Quantization-aware You Only Look Once for Panoptic Driving Perception—0
R2 Loss: Range Restriction Loss for Model Compression and Quantization—0
Radio: Rate-Distortion Optimization for Large Language Model Compression—0
RAG-based User Profiling for Precision Planning in Mixed-precision Over-the-Air Federated Learning—0
Random Binary Mappings for Kernel Learning and Efficient SVM—0
Random Projections with Asymmetric Quantization—0
Random VLAD based Deep Hashing for Efficient Image Retrieval—0
Random Walk Graph Laplacian based Smoothness Prior for Soft Decoding of JPEG Images—0
RAND: Robustness Aware Norm Decay For Quantized Seq2seq Models—0
Rapid Deployment of Domain-specific Hyperspectral Image Processors with Application to Autonomous Driving—0
Rapid yet accurate Tile-circuit and device modeling for Analog In-Memory Computing—0
Rate-aware Compression for NeRF-based Volumetric Video—0
Rate-Constrained Quantization for Communication-Efficient Federated Learning—0
Rate-Distortion-Cognition Controllable Versatile Neural Image Compression—0
Rate distortion comparison of a few gradient quantizers—0
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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