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 4026–4050 of 4925 papers

TitleStatusHype
Realizing a Low-Power Head-Mounted Phase-Only Holographic Display by Light-Weight Compression—0
BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization—0
Accelerating Deep Learning Inference via Freezing—0
Switchable Precision Neural Networks—0
Random VLAD based Deep Hashing for Efficient Image Retrieval—0
Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior—0
Differentially Quantized Gradient Methods—0
Emotion Recognition Using Speaker Cues—0
Automatic Pruning for Quantized Neural Networks—0
Widening and Squeezing: Towards Accurate and Efficient QNNs—0
SQWA: Stochastic Quantized Weight Averaging for Improving the Generalization Capability of Low-Precision Deep Neural Networks—0
Optimal Controller Synthesis and Dynamic Quantizer Switching for Linear-Quadratic-Gaussian Systems—0
Improving LPCNet-based Text-to-Speech with Linear Prediction-structured Mixture Density Network—0
Optimized Feature Space Learning for Generating Efficient Binary Codes for Image Retrieval—0
Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms—0
Deep Learning-based Image Compression with Trellis Coded Quantization—0
Communication Efficient Federated Learning over Multiple Access Channels—0
Real-Time Object Detection and Recognition on Low-Compute Humanoid Robots using Deep Learning—0
Adaptive Dithering Using Curved Markov-Gaussian Noise in the Quantized Domain for Mapping SDR to HDR Image—0
Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks—0
A "Network Pruning Network" Approach to Deep Model Compression—0
Asymmetric Correlation Quantization Hashing for Cross-modal Retrieval—0
Hierarchical Modeling of Multidimensional Data in Regularly Decomposed Spaces: Synthesis and Perspective—0
Deep Optimized Multiple Description Image Coding via Scalar Quantization LearningCode0
Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network ClassifiersCode0
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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