AIMC Topic: Data Compression

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A Novel Deep-Learning Model Compression Based on Filter-Stripe Group Pruning and Its IoT Application.

Sensors (Basel, Switzerland) Jul 27, 2022
Nowadays, there is a tradeoff between the deep-learning module-compression ratio and the module accuracy. In this paper, a strategy for refining the pruning quantification and weights based on neural network filters is proposed. Firstly, filters in t...
Neural Networks, Computer Deep Learning Data Compression Physical Phenomena
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High-Quality Video Watermarking Based on Deep Neural Networks and Adjustable Subsquares Properties Algorithm.

Sensors (Basel, Switzerland) Jul 19, 2022
This paper presents a method of high-capacity and transparent watermarking based on the usage of deep neural networks with the adjustable subsquares properties algorithm to encode the data of a watermark in high-quality video using the H.265/HEVC (Hi...
Neural Networks, Computer Algorithms Video Recording Data Compression
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Auxiliary Pneumonia Classification Algorithm Based on Pruning Compression.

Computational and mathematical methods in medicine Jul 18, 2022
Pneumonia infection is the leading cause of death in young children. The commonly used pneumonia detection method is that doctors diagnose through chest X-ray, and external factors easily interfere with the results. Assisting doctors in diagnosing pn...
Humans Neural Networks, Computer Algorithms Deep Learning Child, Preschool Child Pneumonia Data Compression
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Low-Complexity Adaptive Sampling of Block Compressed Sensing Based on Distortion Minimization.

Sensors (Basel, Switzerland) Jun 25, 2022
Block compressed sensing (BCS) is suitable for image sampling and compression in resource-constrained applications. Adaptive sampling methods can effectively improve the rate-distortion performance of BCS. However, adaptive sampling methods bring hig...
Neural Networks, Computer Image Processing, Computer-Assisted Data Compression
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Towards Convolutional Neural Network Acceleration and Compression Based on -Means.

Sensors (Basel, Switzerland) Jun 6, 2022
Convolutional Neural Networks (CNNs) are popular models that are widely used in image classification, target recognition, and other fields. Model compression is a common step in transplanting neural networks into embedded devices, and it is often use...
Neural Networks, Computer Algorithms Acceleration Data Compression Physical Phenomena
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MobilePrune: Neural Network Compression via Sparse Group Lasso on the Mobile System.

Sensors (Basel, Switzerland) May 27, 2022
It is hard to directly deploy deep learning models on today's smartphones due to the substantial computational costs introduced by millions of parameters. To compress the model, we develop an ℓ0-based sparse group lasso model called MobilePrune which...
Neural Networks, Computer Algorithms Data Compression Physical Phenomena
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LAP: Latency-aware automated pruning with dynamic-based filter selection.

Neural networks : the official journal of the International Neural Network Society May 10, 2022
Model pruning is widely used to compress and accelerate convolutional neural networks (CNNs). Conventional pruning techniques only focus on how to remove more parameters while ensuring model accuracy. This work not only covers the optimization of mod...
Neural Networks, Computer Algorithms Automation Data Compression
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Progressive compressive sensing of large images with multiscale deep learning reconstruction.

Scientific reports May 4, 2022
Compressive sensing (CS) is a sub-Nyquist sampling framework that has been employed to improve the performance of numerous imaging applications during the last 15 years. Yet, its application for large and high-resolution imaging remains challenging i...
Humans Algorithms Deep Learning User-Computer Interface Imaging, Three-Dimensional Data Compression Physical Phenomena
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StructADMM: Achieving Ultrahigh Efficiency in Structured Pruning for DNNs.

IEEE transactions on neural networks and learning systems May 2, 2022
Weight pruning methods of deep neural networks (DNNs) have been demonstrated to achieve a good model pruning rate without loss of accuracy, thereby alleviating the significant computation/storage requirements of large-scale DNNs. Structured weight pr...
Neural Networks, Computer Data Compression
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Communication-efficient federated learning via knowledge distillation.

Nature communications Apr 19, 2022
Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates in each iteration of model learning rather than the ...
Humans Machine Learning Communication Privacy Data Compression
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