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 776–800 of 4925 papers

TitleStatusHype
PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation- and Attention-based NetworkCode1
Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical SystemsCode1
Phrase Retrieval Learns Passage Retrieval, TooCode1
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data AugmentationCode1
PolyFormer: Referring Image Segmentation as Sequential Polygon GenerationCode1
PoseGPT: Quantization-based 3D Human Motion Generation and ForecastingCode1
Position-based Scaled Gradient for Model Quantization and PruningCode1
Deep Transferring QuantizationCode1
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two QuantizationCode1
DFRot: Achieving Outlier-Free and Massive Activation-Free for Rotated LLMs with Refined RotationCode1
CISSIR: Beam Codebooks with Self-Interference Reduction Guarantees for Integrated Sensing and Communication Beyond 5GCode1
Deep Geometry Post-Processing for Decompressed Point CloudsCode1
APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor CoresCode1
PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in ControlCode1
Privacy-Preserving Action Recognition via Motion Difference QuantizationCode1
Deep Learning-Enabled One-Bit DoA EstimationCode1
Compact representations of convolutional neural networks via weight pruning and quantizationCode1
Proximal Mean-field for Neural Network QuantizationCode1
Pruning Large Language Models with Semi-Structural Adaptive Sparse TrainingCode1
Dataset Quantization with Active Learning based Adaptive SamplingCode1
PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision TransformersCode1
DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector QuantizationCode1
PTQ4DiT: Post-training Quantization for Diffusion TransformersCode1
Deep PeNSieve: A deep learning framework based on the posit number systemCode1
DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion 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