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 601–625 of 4925 papers

TitleStatusHype
Efficient Quantized Sparse Matrix Operations on Tensor CoresCode1
Exploring the Connection Between Binary and Spiking Neural NetworksCode1
Fast Lossless Neural Compression with Integer-Only Discrete FlowsCode1
BinaryHPE: 3D Human Pose and Shape Estimation via BinarizationCode1
BED: A Real-Time Object Detection System for Edge DevicesCode1
Fast and Low-Cost Genomic Foundation Models via Outlier RemovalCode1
GAN Slimming: All-in-One GAN Compression by A Unified Optimization FrameworkCode1
DVD-Quant: Data-free Video Diffusion Transformers QuantizationCode1
Fast Nearest Convolution for Real-Time Efficient Image Super-ResolutionCode1
Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANNCode1
Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution NetworksCode1
Benchmarking Quantized Neural Networks on FPGAs with FINNCode1
Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-ResolutionCode1
Feature Quantization Improves GAN TrainingCode1
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and QuantizationCode1
Few shot font generation via transferring similarity guided global style and quantization local styleCode1
DQS3D: Densely-matched Quantization-aware Semi-supervised 3D DetectionCode1
FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher Information Matrix ApproximationCode1
Dynamic Network Quantization for Efficient Video InferenceCode1
Fine-grained Data Distribution Alignment for Post-Training QuantizationCode1
Finite Scalar Quantization: VQ-VAE Made SimpleCode1
Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT CompressionCode1
BAFFLE: A Baseline of Backpropagation-Free Federated LearningCode1
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical StudyCode1
Catastrophic Failure of LLM Unlearning via QuantizationCode1
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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