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pro vyhledávání: '"Jang, Hyemi"'
Estimating the homography between two images is crucial for mid- or high-level vision tasks, such as image stitching and fusion. However, using supervised learning methods is often challenging or costly due to the difficulty of collecting ground-trut
Externí odkaz:
http://arxiv.org/abs/2411.13036
The disparity in accuracy between classes in standard training is amplified during adversarial training, a phenomenon termed the robust fairness problem. Existing methodologies aimed to enhance robust fairness by sacrificing the model's performance o
Externí odkaz:
http://arxiv.org/abs/2401.12532
Although supervised image denoising networks have shown remarkable performance on synthesized noisy images, they often fail in practice due to the difference between real and synthesized noise. Since clean-noisy image pairs from the real world are ex
Externí odkaz:
http://arxiv.org/abs/2310.10088
The aim of continual learning is to learn new tasks continuously (i.e., plasticity) without forgetting previously learned knowledge from old tasks (i.e., stability). In the scenario of online continual learning, wherein data comes strictly in a strea
Externí odkaz:
http://arxiv.org/abs/2302.08741
Generative adversarial networks (GANs) with clustered latent spaces can perform conditional generation in a completely unsupervised manner. In the real world, the salient attributes of unlabeled data can be imbalanced. However, most of existing unsup
Externí odkaz:
http://arxiv.org/abs/2106.05319
Deep neural networks are widely used and exhibit excellent performance in many areas. However, they are vulnerable to adversarial attacks that compromise the network at the inference time by applying elaborately designed perturbation to input data. A
Externí odkaz:
http://arxiv.org/abs/1903.00585
To promote secure and private artificial intelligence (SPAI), we review studies on the model security and data privacy of DNNs. Model security allows system to behave as intended without being affected by malicious external influences that can compro
Externí odkaz:
http://arxiv.org/abs/1807.11655
Akademický článek
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Autor:
Kim, Isak (AUTHOR), Jang, Hyemi (AUTHOR), Kim, So Rin1 (AUTHOR) sorin.kim@umsl.edu, Choi, Jihyeon (AUTHOR)
Publikováno v:
Journal of Child & Adolescent Trauma. Aug2024, p1-12.
Autor:
Jang H; Bio-Chemical Analysis Team, Korea Basic Science Institute, Cheongju, Republic of Korea.; Division of Bio-Analytical Science, University of Science and Technology, Daejeon, Republic of Korea., Choi M; Bio-Chemical Analysis Team, Korea Basic Science Institute, Cheongju, Republic of Korea., Jang KS; Bio-Chemical Analysis Team, Korea Basic Science Institute, Cheongju, Republic of Korea.; Division of Bio-Analytical Science, University of Science and Technology, Daejeon, Republic of Korea.
Publikováno v:
Frontiers in plant science [Front Plant Sci] 2024 Jan 22; Vol. 15, pp. 1333035. Date of Electronic Publication: 2024 Jan 22 (Print Publication: 2024).