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 2101–2125 of 4925 papers

TitleStatusHype
An Analysis on Quantizing Diffusion Transformers—0
Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment—0
Improving Low-Precision Network Quantization via Bin Regularization—0
Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Training—0
Inverted Semantic-Index for Image Retrieval—0
Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval—0
SpeedLimit: Neural Architecture Search for Quantized Transformer Models—0
Activation Map-based Vector Quantization for 360-degree Image Semantic Communication—0
Distilled Low Rank Neural Radiance Field with Quantization for Light Field Compression—0
Analyzing Quantization in TVM—0
Improved Residual Vector Quantization for High-dimensional Approximate Nearest Neighbor Search—0
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics—0
Distance Encoded Product Quantization—0
Distance-aware Quantization—0
Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization—0
Analyzing Compression Techniques for Computer Vision—0
Improved training of binary networks for human pose estimation and image recognition—0
Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models—0
DiskANN++: Efficient Page-based Search over Isomorphic Mapped Graph Index using Query-sensitivity Entry Vertex—0
Analytical aspects of non-differentiable neural networks—0
Disentangling segmental and prosodic factors to non-native speech comprehensibility—0
Bielik 11B v2 Technical Report—0
Analysis of the influence of final resolution on ADC accuracy—0
Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison—0
Disentangled Representation Learning for Unsupervised Neural Quantization—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