Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Youngmin Ro"'
Publikováno v:
IEEE Access, Vol 12, Pp 151856-151863 (2024)
Numerical models have long been used to understand geoscientific phenomena, including tidal currents, crucial for renewable energy production and coastal engineering. However, their computational cost hinders generating data of varying resolutions. A
Externí odkaz:
https://doaj.org/article/e0c8cbf4f581432c81f2b907ef44f1f0
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 17087-17098 (2024)
Multilabel remote sensing image classification is a fundamental task that classifies multiple objects and land covers within an image. However, training deep learning models for this task requires a considerable cost of labeling. While several effort
Externí odkaz:
https://doaj.org/article/98979b5b0c4e4d888b359e50dc8f6587
Autor:
Seokju Yun, Youngmin Ro
Publikováno v:
IEEE Access, Vol 12, Pp 13626-13633 (2024)
In this paper, we propose Dynamic Residual Convolution (DRConv), an efficient method for computing input-specific local features while addressing the limitations of dynamic convolution. DRConv utilizes global salient features calculated using efficie
Externí odkaz:
https://doaj.org/article/dd121168c58648d49fc9fd71800d4622
Autor:
Youngmin Ro, Jin Young Choi
Publikováno v:
IEEE Access, Vol 8, Pp 118525-118533 (2020)
The structure of a multi-head ensemble has been employed by many algorithms in various applications including deep metric learning. However, their structures have been empirically designed in a simple way such as using the same head structure, which
Externí odkaz:
https://doaj.org/article/6f3f0a108bda4d3d98fc01b1a70fa2dd
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:7345-7353
Normalizing flows have shown to be a promising approach to deep generative modeling due to their ability to exactly evaluate density --- other alternatives either implicitly model the density or use approximate surrogate density. In this work, we pre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::afa6803f168c5c463c2c9bc5ef62663b
https://doi.org/10.1609/aaai.v36i7.20697
https://doi.org/10.1609/aaai.v36i7.20697
Various deepfake detectors have been proposed, but challenges still exist to detect images of unknown categories or GAN models outside of the training settings. Such issues arise from the overfitting issue, which we discover from our own analysis and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e260cb84fd5f2936abdab275f6369a6
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031197802
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2353ae466a385592343fd5cc4b61b15f
https://doi.org/10.1007/978-3-031-19781-9_5
https://doi.org/10.1007/978-3-031-19781-9_5
Autor:
Jin Young Choi, Youngmin Ro
Publikováno v:
IEEE Access, Vol 8, Pp 118525-118533 (2020)
The structure of a multi-head ensemble has been employed by many algorithms in various applications including deep metric learning. However, their structures have been empirically designed in a simple way such as using the same head structure, which
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d963c6b9c809315882fd15119dc68776
https://doi.org/10.1109/access.2020.3004579
https://doi.org/10.1109/access.2020.3004579
Publikováno v:
IEEE transactions on neural networks and learning systems. 33(9)
Image retrieval is a challenging problem that requires learning generalized features enough to identify untrained classes, even with very few classwise training samples. In this article, to obtain generalized features further in learning retrieval da
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e500e5f423e58cb575bf28fd20ed1238
https://pubmed.ncbi.nlm.nih.gov/33657002
https://pubmed.ncbi.nlm.nih.gov/33657002
Autor:
Youngmin Ro, Jin Young Choi
Existing fine-tuning methods use a single learning rate over all layers. In this paper, first, we discuss that trends of layer-wise weight variations by fine-tuning using a single learning rate do not match the well-known notion that lower-level laye
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b7e9e49fe698a0c63a8527cdd4d557af
http://arxiv.org/abs/2002.06048
http://arxiv.org/abs/2002.06048