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 2801–2825 of 4925 papers

TitleStatusHype
Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks—0
DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement—0
Dirichlet Mixture Model based VQ Performance Prediction for Line Spectral Frequency—0
Disco Intelligent Omni-Surfaces: 360-degree Fully-Passive Jamming Attacks—0
Disco Intelligent Reflecting Surfaces: Active Channel Aging for Fully-Passive Jamming Attacks—0
DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding—0
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference—0
Discovering Patterns in Time-Varying Graphs: A Triclustering Approach—0
Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition—0
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving—0
Discrete-Valued Neural Communication—0
Discrete-Valued Neural Networks Using Variational Inference—0
Discriminative Cross-View Binary Representation Learning—0
Disentangled Representation Learning for Unsupervised Neural Quantization—0
Disentangling segmental and prosodic factors to non-native speech comprehensibility—0
DiskANN++: Efficient Page-based Search over Isomorphic Mapped Graph Index using Query-sensitivity Entry Vertex—0
Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models—0
Distance-aware Quantization—0
Distance Encoded Product Quantization—0
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics—0
Distilled Low Rank Neural Radiance Field with Quantization for Light Field Compression—0
Robust building footprint extraction from big multi-sensor data using deep competition network—0
Robust Dequantized Compressive Sensing—0
Robust Downlink Transmit Optimization under Quantized Channel Feedback via the Strong Duality for QCQP—0
RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems—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