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11 thg 12, 2024 · Data imputation has increasingly gained attention due to its critical role in enhancing data quality and accuracy. However, traditional imputation methods often
26 thg 4, 2021 · In this study, we propose a generative adversarial network (GAN)-based deep learning approach for road segmentation from high-resolution aerial imagery. In the generative …
21 thg 9, 2021 · A Gated Generative Adversarial Imputation Approach for Signalized Road Networks Published in: IEEE Transactions on Intelligent Transportation Systems ( Volume: 23 …
10 thg 8, 2021 · As one of the effective imputation approaches, generative adversarial networks (GANs) are implicit generative models that can be used for data imputation, which is …
11 thg 7, 2024 · Specifically, a generative adversarial network is introduced to learn high-dimensional feature that is domain-invariant in two data domains. In addition, a pre-training …
19 thg 10, 2021 · We present GA-GAN (Graph Aggregate Generative Adversarial Network), consisting of graph sample and aggregate (GraphSAGE) and a generative adversarial network …
26 thg 1, 2024 · Therefore, a new specific emitter missing data imputation model is proposed, which is called bidirectional stackable recurrent generative adversarial imputation network …
24 thg 2, 2022 · As a classic deep learning method, Generative Adversarial Network (GAN) achieves remarkable success in image recovery fields, which opens up a new way for the …
15 thg 6, 2024 · Abstract: We propose a new method for missing data imputation in smart meters in the distribution power system based on Generative Adversarial Networks. Firstly, the mask …
24 thg 4, 2019 · In this paper, we propose a novel approach using parallel data and generative adversarial networks (GANs) to enhance traffic data imputation. Parallel data is a recently …
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