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Efficient Deep Learning Model Compression for Sensor-Based Vision Systems via Outlier-Aware Quantization [PDF]
SensorsWith the rapid growth of sensor technology and computer vision, efficient deep learning models are essential for real-time image feature extraction in resource-constrained environments.
Joonhyuk Yoo, Guenwoo Ban
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Removing Zero Variance Units of Deep Models for COVID-19 Detection
IEEE Access, 2023 Deep Learning has been used for several applications including the analysis of medical images. Some transfer learning works show that an improvement in performance is obtained if a pre-trained model on ImageNet is transferred to a new task.
Jesus Garcia-Ramirez +2 more
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IEEE Access, 2023 Our previous research applied a novel classification-integrated moving average (CIMA) method, an intelligence method that improves the performance of passive infrared (PIR) sensors in smart lighting to make control more comfortable for the user. However,
Aji Gautama Putrada +3 more
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Automatic Detection of Generated Texts and Energy: Exploring the Relationship [PDF]
E3S Web of Conferences, 2023 The proliferation of artificial intelligence (AI) and natural language processing (NLP) technologies has enabled the generation of realistic and coherent texts, but it also raises concerns regarding the potential misuse of these technologies for ...
Al Karkouri Adnane +2 more
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Compression-based Facies Modelling
Mathematical Geosciences, 2023 AbstractSimple object- or pixel-based facies models use facies proportions as the constraining input parameter to be honored in the output model. The resultant interconnectivity of the facies bodies is an unconstrained output property of the modelling, and if the objects being modelled are geometrically representative in three dimensions, commonly ...
Tom Manzocchi +3 more
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Combine-Net: An Improved Filter Pruning Algorithm
Information, 2021 The powerful performance of deep learning is evident to all. With the deepening of research, neural networks have become more complex and not easily generalized to resource-constrained devices.
Jinghan Wang, Guangyue Li, Wenzhao Zhang
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Neural Network Compression via Low Frequency Preference
Remote Sensing, 2023 Network pruning has been widely used in model compression techniques, and offers a promising prospect for deploying models on devices with limited resources.
Chaoyan Zhang +3 more
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Graph pruning for model compression [PDF]
Applied Intelligence, 2022 accepted by Applied ...
Mingyang Zhang +3 more
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, 2021 With time, machine learning models have increased in their scope, functionality and size. Consequently, the increased functionality and size of such models requires high-end hardware to both train and provide inference after the fact. This paper aims to explore the possibilities within the domain of model compression, discuss the efficiency of ...
Ishtiaq, Arhum +3 more
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DP Compress: A Model Compression Scheme for Generating Efficient Deep Potential Models
Journal of Chemical Theory and Computation, 2022 Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structure schemes, these models suffer from costly computations via deep neural networks to predict the energy and atomic forces, resulting in lower running efficiency as compared to
Denghui Lu +6 more
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