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

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Showing 4001–4050 of 4925 papers

TitleStatusHype
Kernel Quantization for Efficient Network Compression—0
Communication-efficient Variance-reduced Stochastic Gradient Descent—0
Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback—0
Optimizing JPEG Quantization for Classification Networks—0
Cluster Pruning: An Efficient Filter Pruning Method for Edge AI Vision Applications—0
A Survey on Deep Hashing Methods—0
Phoenix: A Low-Precision Floating-Point Quantization Oriented Architecture for Convolutional Neural Networks—0
Image Hashing by Minimizing Discrete Component-wise Wasserstein DistanceCode0
WaveQ: Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization—0
Quantized Neural Network Inference with Precision Batching—0
Moniqua: Modulo Quantized Communication in Decentralized SGD—0
Adversarial Attack on Deep Product Quantization Network for Image Retrieval—0
Optimal Gradient Quantization Condition for Communication-Efficient Distributed Training—0
Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees—0
Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks—0
OptComNet: Optimized Neural Networks for Low-Complexity Channel Estimation—0
Revisiting Saliency Metrics: Farthest-Neighbor Area Under CurveCode0
PoET-BiN: Power Efficient Tiny Binary Neurons—0
Quantized Decentralized Stochastic Learning over Directed Graphs—0
New Bounds For Distributed Mean Estimation and Variance Reduction—0
Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor—0
Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision—0
Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems—0
SYMOG: learning symmetric mixture of Gaussian modes for improved fixed-point quantization—0
Gradient _1 Regularization for Quantization Robustness—0
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