Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Gukyeong Kwon"'
Autor:
Yash-Yee Logan, Kiran Kokilepersaud, Gukyeong Kwon, Ghassan AlRegib, Charles Wykoff, Hannah Yu
In this paper, we propose a framework that incorporates experts diagnostics and insights into the analysis of Optical Coherence Tomography (OCT) using multi-modal learning. To demonstrate the effectiveness of this approach, we create a medical diagno
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc6db7a8d27f28a931a31498738c6acc
Autor:
Zhaowei Cai, Gukyeong Kwon, Avinash Ravichandran, Erhan Bas, Zhuowen Tu, Rahul Bhotika, Stefano Soatto
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200588
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bb57ff6486d1fc396ff9335deef64781
https://doi.org/10.1007/978-3-031-20059-5_17
https://doi.org/10.1007/978-3-031-20059-5_17
Publikováno v:
ICIP
In this paper, we propose a model-based characterization of neural networks to detect novel input types and conditions. Novelty detection is crucial to identify abnormal inputs that can significantly degrade the performance of machine learning algori
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c97cfce7e62e255c1d898e47d94858ad
http://arxiv.org/abs/2008.06094
http://arxiv.org/abs/2008.06094
Publikováno v:
ICIP
Visual explanations are logical arguments based on visual features that justify the predictions made by neural networks. Current modes of visual explanations answer questions of the form $`Why \text{ } P?'$. These $Why$ questions operate under broad
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::103f2868d1d7952eb73814e681925a96
Publikováno v:
Computer Vision – ECCV 2020 ISBN: 9783030585884
ECCV (21)
ECCV (21)
Learning representations that clearly distinguish between normal and abnormal data is key to the success of anomaly detection. Most of existing anomaly detection algorithms use activation representations from forward propagation while not exploiting
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::096b0bd74a63d624002f704279baf52c
https://doi.org/10.1007/978-3-030-58589-1_13
https://doi.org/10.1007/978-3-030-58589-1_13
Autor:
Gukyeong, Kwon, Ghassan Al, Regib
Publikováno v:
IEEE Transactions on Image Processing. :1-1
Generalized zero-shot learning (GZSL) aims at training a model that can generalize to unseen class data by only using auxiliary information. One of the main challenges in GZSL is a biased model prediction toward seen classes caused by overfitting on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b02aa004e0122716f4a12b7ffd687686
https://doi.org/10.1109/tip.2022.3153138
https://doi.org/10.1109/tip.2022.3153138
Publikováno v:
ICIP
In this paper, we utilize weight gradients from backpropagation to characterize the representation space learned by deep learning algorithms. We demonstrate the utility of such gradients in applications including perceptual image quality assessment a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::220ce841213f7473c2bdb87135cc9933
https://doi.org/10.1109/icip.2019.8803228
https://doi.org/10.1109/icip.2019.8803228
Publikováno v:
ICIP
In this paper, we generate and control semantically interpretable filters that are directly learned from natural images in an unsupervised fashion. Each semantic filter learns a visually interpretable local structure in conjunction with other filters
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::84c2b97007aefa95786126d8d9ef52a5
Publikováno v:
ICME
We propose a perceptual video quality assessment (PVQA) metric for distorted videos by analyzing the power spectral density (PSD) of a group of pictures. This is an estimation approach that relies on the changes in video dynamic calculated in the fre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07380075e1245b173cc92a12a518a39f