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Akademický článek

Model-agnostic meta-learning-based region-adaptive parameter adjustment scheme for influenza forecasting

Autor: Jaeuk Moon, Yoona Noh, Sungwoo Park, Eenjun Hwang

Publikováno v: Journal of King Saud University: Computer and Information Sciences, Vol 35, Iss 1, Pp 175-184 (2023)

Deep learning models perform well when there is enough data available for training, but otherwise the performance deteriorates rapidly owing to the so-called data shortage problem. Recently, model-agnostic meta-learning (MAML) was proposed to allevia

Externí odkaz: https://doaj.org/article/d5df59cb52f44f1a9fd1f7f9925c0b76

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Akademický článek

A Task-Adaptive Parameter Transformation Scheme for Model-Agnostic-Meta-Learning-Based Few-Shot Animal Sound Classification

Autor: Jaeuk Moon, Eunbeen Kim, Junha Hwang, Eenjun Hwang

Publikováno v: Applied Sciences, Vol 14, Iss 3, p 1025 (2024)

Deep learning models that require vast amounts of training data struggle to achieve good animal sound classification (ASC) performance. Among recent few-shot ASC methods to address the data shortage problem regarding animals that are difficult to obs

Externí odkaz: https://doaj.org/article/c5ec124c540f435f90cfd0036824783e

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Akademický článek

Conditional Tabular GAN-Based Two-Stage Data Generation Scheme for Short-Term Load Forecasting

Autor: Jaeuk Moon, Seungwon Jung, Sungwoo Park, Eenjun Hwang

Publikováno v: IEEE Access, Vol 8, Pp 205327-205339 (2020)

Load forecasting is one of the critical tasks for enhancing the energy efficiency of smart grids. Even though recent deep learning-based load forecasting models have shown excellent forecasting performance, one of the common problems they faced was t

Externí odkaz: https://doaj.org/article/b2ff634c67e340009620adfc476db4d5

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Akademický článek

DualDiscWaveGAN-Based Data Augmentation Scheme for Animal Sound Classification

Autor: Eunbeen Kim, Jaeuk Moon, Jonghwa Shim, Eenjun Hwang

Publikováno v: Sensors, Vol 23, Iss 4, p 2024 (2023)

Animal sound classification (ASC) refers to the automatic identification of animal categories by sound, and is useful for monitoring rare or elusive wildlife. Thus far, deep-learning-based models have shown good performance in ASC when training data

Externí odkaz: https://doaj.org/article/02b621c7962f42baa8f81e30b3aa3499

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