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pro vyhledávání: '"Lee, Janghyeon"'
Trajectory anomaly detection is crucial for effective decision-making in urban and human mobility management. Existing methods of trajectory anomaly detection generally focus on training a trajectory generative model and evaluating the likelihood of
Externí odkaz:
http://arxiv.org/abs/2410.19136
Autor:
Lee, JangHyeon, Kim, Lawrence H.
Smartphones are integral to modern life, yet research highlights the cognitive drawbacks associated even with their mere presence. Physically removing them from sight is a solution, but it is sometimes impractical and may increase anxiety due to fear
Externí odkaz:
http://arxiv.org/abs/2403.03875
Transfer learning is widely used for training deep neural networks (DNN) for building a powerful representation. Even after the pre-trained model is adapted for the target task, the representation performance of the feature extractor is retained to s
Externí odkaz:
http://arxiv.org/abs/2308.09971
Autor:
Lee, Janghyeon, Kim, Jongsuk, Shon, Hyounguk, Kim, Bumsoo, Kim, Seung Hwan, Lee, Honglak, Kim, Junmo
Pre-training vision-language models with contrastive objectives has shown promising results that are both scalable to large uncurated datasets and transferable to many downstream applications. Some following works have targeted to improve data effici
Externí odkaz:
http://arxiv.org/abs/2209.13430
Pre-trained representation is one of the key elements in the success of modern deep learning. However, existing works on continual learning methods have mostly focused on learning models incrementally from scratch. In this paper, we explore an altern
Externí odkaz:
http://arxiv.org/abs/2208.08112
We present a novel approach for oriented object detection, named TricubeNet, which localizes oriented objects using visual cues ($i.e.,$ heatmap) instead of oriented box offsets regression. We represent each object as a 2D Tricube kernel and extract
Externí odkaz:
http://arxiv.org/abs/2104.11435
We propose a quadratic penalty method for continual learning of neural networks that contain batch normalization (BN) layers. The Hessian of a loss function represents the curvature of the quadratic penalty function, and a Kronecker-factored approxim
Externí odkaz:
http://arxiv.org/abs/2004.07507
We propose a novel continual learning method called Residual Continual Learning (ResCL). Our method can prevent the catastrophic forgetting phenomenon in sequential learning of multiple tasks, without any source task information except the original n
Externí odkaz:
http://arxiv.org/abs/2002.06774
Publikováno v:
In Journal of Hazardous Materials 15 June 2020 392
Akademický článek
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