Výsledky vyhledávání - "Seok-Jun Buu"

Akademický článek

Disentangled Prototype-Guided Dynamic Memory Replay for Continual Learning in Acoustic Signal Classification

Autor: Seok-Hun Choi, Seok-Jun Buu

Publikováno v: IEEE Access, Vol 12, Pp 153796-153808 (2024)

Acoustic signal classification in continual learning environments faces significant challenges, particularly due to catastrophic forgetting and the need for efficient memory utilization. Memory replay techniques, though foundational, often struggle t

Externí odkaz: https://doaj.org/article/a2ef1ba506b84226a9fa0cb0b9e82fd3

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Akademický článek

Deep Generative Replay With Denoising Diffusion Probabilistic Models for Continual Learning in Audio Classification

Autor: Hyeon-Ju Lee, Seok-Jun Buu

Publikováno v: IEEE Access, Vol 12, Pp 134714-134727 (2024)

Accurate classification of audio data is essential in various fields such as speech recognition, safety management, healthcare, security, and surveillance. However, existing deep learning classifiers typically require extensive pre-collected data and

Externí odkaz: https://doaj.org/article/19a4a9d83a1e400b846c3e4dad5c8037

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Akademický článek

Graph Anomaly Detection With Disentangled Prototypical Autoencoder for Phishing Scam Detection in Cryptocurrency Transactions

Autor: Junha Kang, Seok-Jun Buu

Publikováno v: IEEE Access, Vol 12, Pp 91075-91088 (2024)

As the popularity of cryptocurrencies grows, the threat of phishing scams on trading networks is growing. Detecting unusual transactions within the complex structure of these transaction graphs and imbalanced data between Benign and Scams remains a v

Externí odkaz: https://doaj.org/article/c0be14a0cb0040f68a53bafb7aad9dca

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Akademický článek

Disentangled Prototypical Convolutional Network for Few-Shot Learning in In-Vehicle Noise Classification

Autor: Robin Inho Kee, Dahyun Nam, Seok-Jun Buu, Sung-Bae Cho

Publikováno v: IEEE Access, Vol 12, Pp 66801-66808 (2024)

This study addresses the persistent challenge of in-vehicle noise, a significant factor affecting customer satisfaction and safety in the automotive industry. Despite advancements in understanding various noise sources and mitigation strategies, vehi

Externí odkaz: https://doaj.org/article/cd279119e62345bf95e61414616ed33e

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Akademický článek

Anchor-Net: Distance-Based Self-Supervised Learning Model for Facial Beauty Prediction

Autor: Jiho Bae, Seok-Jun Buu, Suwon Lee

Publikováno v: IEEE Access, Vol 12, Pp 61375-61387 (2024)

In today’s society, beauty is more than just aesthetics, it has a profound impact on many aspects of life, including social interactions, self-confidence, and job opportunities. To quantify this beauty, the field of Facial Beauty Prediction (FBP) i

Externí odkaz: https://doaj.org/article/6993d80e4b55446ab14e75a883b95bd7

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