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 2576–2600 of 4925 papers

TitleStatusHype
Compression of Deep Neural Networks for Image Instance Retrieval—0
Compression of Deep Neural Networks on the Fly—0
Compression of Generative Pre-trained Language Models via Quantization—0
Compression of Recurrent Neural Networks for Efficient Language Modeling—0
Compression of Site-Specific Deep Neural Networks for Massive MIMO Precoding—0
Compression-Realized Deep Structural Network for Video Quality Enhancement—0
Compression Scaling Laws:Unifying Sparsity and Quantization—0
Compression strategies and space-conscious representations for deep neural networks—0
Compression without Quantization—0
Compressive Beam Alignment for Indoor Millimeter-Wave Systems—0
Compressive Estimation of a Stochastic Process with Unknown Autocorrelation Function—0
Compressive Quantization for Fast Object Instance Search in Videos—0
Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications—0
Compressive Spectrum Sensing with 1-bit ADCs—0
Compress Polyphone Pronunciation Prediction Model with Shared Labels—0
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt—0
Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization—0
Computational Complexity Evaluation of Neural Network Applications in Signal Processing—0
Computation-Efficient Quantization Method for Deep Neural Networks—0
Compute-Optimal LLMs Provably Generalize Better With Scale—0
Computing with Hypervectors for Efficient Speaker Identification—0
Conditional Distribution Quantization in Machine Learning—0
Conditionally Deep Hybrid Neural Networks Across Edge and Cloud—0
CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks—0
Constrained Approximate Similarity Search on Proximity Graph—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