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 2676–2700 of 4925 papers

TitleStatusHype
Robustness and Approximation of Discrete-time Mean-field Games under Discounted Cost Criterion—0
Robustness-aware 2-bit quantization with real-time performance for neural network—0
Robustness-Guided Image Synthesis for Data-Free Quantization—0
Robustness of Neural Networks to Parameter Quantization—0
Robustness to distribution shifts of compressed networks for edge devices—0
Robust Precoding for Multi-User Visible Light Communications with Quantized Channel Information—0
Robust Product Markovian Quantization—0
Robust Semantic Communications Against Semantic Noise—0
Robust Symbol Level Precoding for Overlay Cognitive Radio Networks—0
Robust Task-Specific Beamforming with Low-Resolution ADCs for Power-Efficient Hybrid MIMO Receivers—0
Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging—0
Robust Training of Neural Networks at Arbitrary Precision and Sparsity—0
Robust Vector Quantized-Variational Autoencoder—0
ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models—0
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models—0
Rotate, Clip, and Partition: Towards W2A4KV4 Quantization by Integrating Rotation and Learnable Non-uniform Quantizer—0
Rotated Runtime Smooth: Training-Free Activation Smoother for accurate INT4 inference—0
RotateKV: Accurate and Robust 2-Bit KV Cache Quantization for LLMs via Outlier-Aware Adaptive Rotations—0
Rotation Consistent Margin Loss for Efficient Low-Bit Face Recognition—0
RPR: Random Partition Relaxation for Training; Binary and Ternary Weight Neural Networks—0
RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation—0
RQP-SGD: Differential Private Machine Learning through Noisy SGD and Randomized Quantization—0
RTF-Q: Efficient Unsupervised Domain Adaptation with Retraining-free Quantization—0
RTN: Reparameterized Ternary Network—0
RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content—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