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 826–850 of 4925 papers

TitleStatusHype
Deep Learning-Enabled One-Bit DoA EstimationCode1
Deep Transferring QuantizationCode1
Deep Geometry Post-Processing for Decompressed Point CloudsCode1
Dataset Quantization with Active Learning based Adaptive SamplingCode1
Data-Free Network Quantization With Adversarial Knowledge DistillationCode1
Data-Free Quantization Through Weight Equalization and Bias CorrectionCode1
Conditional Coding and Variable Bitrate for Practical Learned Video CodingCode1
Rate-Adaptive Quantization: A Multi-Rate Codebook Adaptation for Vector Quantization-based Generative ModelsCode1
RawHash2: Mapping Raw Nanopore Signals Using Hash-Based Seeding and Adaptive QuantizationCode1
Representing Domain-Mixing Optical Degradation for Real-World Computational Aberration Correction via Vector QuantizationCode1
DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector QuantizationCode1
AQD: Towards Accurate Fully-Quantized Object DetectionCode1
Reconciling Security and Communication Efficiency in Federated LearningCode1
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two QuantizationCode1
Going Beyond Classification Accuracy Metrics in Model CompressionCode1
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image TranslationCode1
RepQ-ViT: Scale Reparameterization for Post-Training Quantization of Vision TransformersCode1
Resolution Switchable Networks for Runtime Efficient Image RecognitionCode1
Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference PipelineCode1
Communication-Efficient Adaptive Federated LearningCode1
D^2-DPM: Dual Denoising for Quantized Diffusion Probabilistic ModelsCode1
COMQ: A Backpropagation-Free Algorithm for Post-Training QuantizationCode1
CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-ResolutionCode1
RGB-Event Fusion with Self-Attention for Collision PredictionCode1
CPLLM: Clinical Prediction with Large Language ModelsCode1
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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