Search Results - subject:"generative adversarial network"

Deepfakes - When we can no longer believe our eyes and ears: Media manipulation in conflict. Challenges and responses

Kleemann, Aldo - 2023

Deception and media manipulation have always been an integral part of wartime propaganda. But never before has it been so easy to create high-quality fabrications of images as well as sound and video recordings. The tendency to react emotionally to these media opens up a whole new possibility...

Persistent link: https://www.econbiz.de/10014431711

Deepfakes - Wenn wir unseren Augen und Ohren nicht mehr trauen können: Medienmanipulationen im Konflikt. Herausforderungen und Bewältigungsstrategien

Kleemann, Aldo - 2023

Täuschung und Medienmanipulation sind seit jeher fester Bestandteil der Kriegskommunikation. Nie zuvor aber war es derart einfach, qualitativ hochwertige Fälschungen von Ton-, Bild- und Videoaufzeichnungen zu erstellen. Die menschliche Neigung, emotional auf diese Medien zu reagieren,...

Persistent link: https://www.econbiz.de/10014431770

Deepfakes - wenn wir unseren Augen und Ohren nicht mehr trauen können : Medienmanipulationen im Konflikt : Herausforderungen und Bewältigungsstrategien

Kleemann, Aldo - Stiftung Wissenschaft und Politik - 2023

Persistent link: https://www.econbiz.de/10014424248

Deepfakes - when we can no longer believe our eyes and ears : media manipulation in conflict : challenges and responses

Kleemann, Aldo - Stiftung Wissenschaft und Politik - 2023

Persistent link: https://www.econbiz.de/10014424351

Ensemble multitask prediction of air pollutants time series : based on variational inference, data projection, and generative adversarial network

Wang, Kang; Qu, Chao; Wang, Jianzhou; Li, Zhiwu; Lu, Haiyan - In: Journal of forecasting 44 (2025) 2, pp. 646-675

Persistent link: https://www.econbiz.de/10015374075

DeepPricing : pricing convertible bonds based on financial time-series generative adversarial networks

Tan, Xiaoyu; Zhang, Zili; Zhao, Xuejun; Wang, Shuyi - In: Financial innovation : FIN 8 (2022), pp. 1-38

Convertible bonds are an important segment of the corporate bond market, however, as hybrid instruments, convertible bonds are difficult to value because they depend on variables related to the underlying stock, the fixed-income part, and the interaction between these components. Besides,...

Persistent link: https://www.econbiz.de/10013272634

Application of deep learning model incorporating domain knowledge in international migration forecasting

Pu, Tongzheng; Huang, Chongxing; Zhang, Haimo; Yang, … - In: Data Technologies and Applications 58 (2024) 5, pp. 787-806

, reaching 0.9855 compared to long short-term memory (LSTM), gated recurrent unit, generative adversarial network (GAN) and the … conditional generative adversarial neural network model incorporating the migration knowledge – conditional generative adversarial … network (MK-CGAN). By using the migration knowledge to design the parameters, MK-CGAN can effectively address the limited data …

Persistent link: https://www.econbiz.de/10015342721

Alleviating class imbalance in actuarial applications using generative adversarial networks

Ngwenduna, Kwanda Sydwell; Mbuvha, Rendani - In: Risks 9 (2021) 3, pp. 1-33

data. In this work, we show how an implicit model using the Generative Adversarial Network (GAN) can alleviate these …

Persistent link: https://www.econbiz.de/10013200718

Alleviating class imbalance in actuarial applications using generative adversarial networks

Ngwenduna, Kwanda Sydwell; Mbuvha, Rendani - In: Risks : open access journal 9 (2021) 3, pp. 1-33

data. In this work, we show how an implicit model using the Generative Adversarial Network (GAN) can alleviate these …

Persistent link: https://www.econbiz.de/10012508505

Mapping the technological landscape of emerging industry value chain through a patent lens : an integrated framework with deep learning

Xu, Guannan; Dong, Fang; Feng, Jiawen - In: IEEE transactions on engineering management : EM 69 (2022) 6,1, pp. 3367-3378

Persistent link: https://www.econbiz.de/10013545629