Zobrazeno 1 - 10
of 1 194
pro vyhledávání: '"Byung-A Song"'
Publikováno v:
IEEE Access, Vol 12, Pp 109690-109701 (2024)
Deep neural networks are generally very vulnerable to adversarial attacks. In order to defend against adversarial attacks in classifiers, Adversarial Purification (AP) was developed to neutralize adversarial perturbations using a generative model at
Externí odkaz:
https://doaj.org/article/30bcbd208fbd48fca32db7b3dae85f4d
Autor:
Seongho Kim, Byung Cheol Song
Publikováno v:
IEEE Access, Vol 12, Pp 72860-72870 (2024)
Ultrasound images acquired through various measuring devices may have different styles, and each style may be specialized for diagnosing specific diseases. Accordingly, ultrasound image-to-image translation (US I2I) has become an essential research f
Externí odkaz:
https://doaj.org/article/2961e611bdf048aab829254e2bac3820
Publikováno v:
IEEE Access, Vol 12, Pp 67847-67859 (2024)
This paper presents an identity-invariant facial expression recognition framework. It aims to make a facial expression recognition (FER) model independently understand facial expressions and identity (ID) attributes such as gender, age, and skin, whi
Externí odkaz:
https://doaj.org/article/74beb1a28a3246c681992a254290e720
Publikováno v:
IEEE Access, Vol 12, Pp 29154-29165 (2024)
Real-time Monte Carlo denoising aims to denoise a 1spp-rendered image with a limited time budget. Many latest techniques for real-time Monte Carlo denoising utilize temporal accumulation (TA) as a pre-processing to improve the temporal stability of s
Externí odkaz:
https://doaj.org/article/8307200eaf104d5494bdd05375e1a37f
Publikováno v:
IEEE Access, Vol 12, Pp 15016-15025 (2024)
In general, salient object detection (SOD) datasets have ambiguity due to annotation accuracy and human subjectivity in determining saliency. Since this data uncertainty causes inaccurate prediction, many techniques tackling data uncertainty have bee
Externí odkaz:
https://doaj.org/article/9497c4af4f034453bc921dd626512d09
Autor:
Xingshu Li, Jae Sang Oh, Yoonji Lee, Eun Chae Lee, Mengyao Yang, Nahyun Kwon, Tae Won Ha, Dong-Yong Hong, Yena Song, Hyun Kyu Kim, Byung Hoo Song, Sun Choi, Man Ryul Lee, Juyoung Yoon
Publikováno v:
Biomaterials Research, Vol 27, Iss 1, Pp 1-14 (2023)
Abstract Background Malignant glioma is among the most lethal and frequently occurring brain tumors, and the average survival period is 15 months. Existing chemotherapy has low tolerance and low blood-brain barrier (BBB) permeability; therefore, the
Externí odkaz:
https://doaj.org/article/f6d475a1cc604be5a697405a2a455ee8
Publikováno v:
IEEE Access, Vol 11, Pp 112015-112026 (2023)
This paper presents a framework for effectively applying knowledge distillation (KD) to super-resolution (SR) tasks for computer graphics (CG) images. Specifically, we propose TAKDSR, a KD framework for SR using a teacher assistant (TA) network. Rece
Externí odkaz:
https://doaj.org/article/5b4a2c0da71844c18f976431aba28a29
Publikováno v:
IEEE Access, Vol 11, Pp 107496-107510 (2023)
Quick commerce has recently become the most important issue in the logistics industry due to intensifying competition for a faster delivery. A fulfillment service that integrates various logistics processes has emerged to conduct delivery service as
Externí odkaz:
https://doaj.org/article/48793676cdd649c593b3293212bf57d2
Publikováno v:
IEEE Access, Vol 11, Pp 87902-87916 (2023)
An image in a display device under strong illuminance can be perceived as darker than the original due to the nature of the human visual system (HVS). In order to alleviate this degradation in terms of software, existing schemes employ global luminan
Externí odkaz:
https://doaj.org/article/29f8795282d14ffdb63f71070ddc1932
Publikováno v:
Systems, Vol 12, Iss 3, p 69 (2024)
Disaster management requires efficient allocation of essential facilities with consideration of various objectives. During the response and recovery phase of disaster management (RRDM), various types of missions occur in multiple periods, and each of
Externí odkaz:
https://doaj.org/article/8e56aa1d130246baa92bcba582d1f267