Results 21 to 30 of about 3,028,772 (307)
A Case for Soft Loss Functions [PDF]
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2020 Recently, Peterson et al. provided evidence of the benefits of using probabilistic soft labels generated from crowd annotations for training a computer vision model, showing that using such labels maximizes performance of the models over unseen data. In this paper, we generalize these results by showing that training with soft labels is an effective ...
Uma, Alexandra +5 more
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Pan-Sharpening Based on Panchromatic Colorization Using WorldView-2
IEEE Access, 2021 In order to overcome the lack of the multispectral image (MS) and adequately preserve the spatial information of panchromatic (PAN) image and the spectral information of MS image, this study proposes a method which adds the spectral information of the ...
Zhangxi Xiong +3 more
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Prediction of Interest Rate Using Artificial Neural Network and Novel Meta-Heuristic Algorithms [PDF]
Iranian Journal of Accounting, Auditing & Finance, 2021 One of the most parameters and variables in every economics is the interest rate. Government officials and lawmakers change interest rates for various purposes: controlling liquidity, inflation, and prices, Economic growth and development, lending, etc ...
Milad Shahvaroughi Farahani
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Neurologic Clinics, 2010 Neurologists frequently evaluate patients complaining of vision loss, especially when the patient has been examined by an ophthalmologist who has found no ocular disease. A significant proportion of patients presenting to the neurologist with visual complaints have nonorganic or functional visual loss. Although there are examination techniques that can
Beau B, Bruce, Nancy J, Newman
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High-Speed Rail Tunnel Panoramic Inspection Image Recognition Technology Based on Improved YOLOv5
Sensors, 2023 In order to meet the fast and accurate automatic detection requirements of equipment maintenance in railway tunnels in the era of high-speed railways, as well as adapting to the high dynamic, low-illumination imaging environment formed by strong light at
Yixin Duan +4 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Training deep neural networks is inherently subject to the predefined and fixed loss functions during optimizing. To improve learning efficiency, we develop Stochastic Loss Function (SLF) to dynamically and automatically generating appropriate gradients to train deep networks in the same round of back-propagation, while maintaining the completeness and
Qingliang Liu 0002, Jinmei Lai
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Citrus Identification and Counting Algorithm Based on Improved YOLOv5s and DeepSort
Agronomy, 2023 A method for counting the number of citrus fruits based on the improved YOLOv5s algorithm combined with the DeepSort tracking algorithm is proposed to address the problem of the low accuracy of counting citrus fruits due to shading and lighting factors ...
Yuhan Lin +3 more
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Loss functions for finite sets
Computational Optimization and Applications, 2022 This paper studies loss functions for finite sets. For a given finite set $S$, we give sum-of-square type loss functions of minimum degree. When $S$ is the vertex set of a standard simplex, we show such loss functions have no spurious minimizers (i.e., every local minimizer is a global one). Up to transformations, we give similar loss functions without
Jiawang Nie, Suhan Zhong
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Forecasting for Battery Storage: Choosing the Error Metric
Energies, 2021 We describe our approach to the Western Power Distribution (WPD) Presumed Open Data (POD) 6 MWh battery storage capacity forecasting competition, in which we finished second.
Colin Singleton, Peter Grindrod
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Remote Sensing, 2023 The deep learning technique has already been successfully applied in the field of microwave remote sensing. Especially, convolutional neural networks have demonstrated remarkable effectiveness in synthetic aperture radar (SAR) image semantic segmentation.
Rui Guo +4 more
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