Zobrazeno 1 - 10
of 158
pro vyhledávání: '"Zheng, Haiyong"'
Autor:
Shi, Shangshang, Wang, Zhimin, Li, Jiaxin, Li, Yanan, Shang, Ruimin, Zheng, Haiyong, Zhong, Guoqiang, Gu, Yongjian
The self-attention mechanism (SAM) has demonstrated remarkable success in various applications. However, training SAM on classical computers becomes computationally challenging as the number of trainable parameters grows. Quantum neural networks (QNN
Externí odkaz:
http://arxiv.org/abs/2305.15680
Autor:
Li, Yanan, Wang, Zhimin, Han, Rongbing, Shi, Shangshang, Li, Jiaxin, Shang, Ruimin, Zheng, Haiyong, Zhong, Guoqiang, Gu, Yongjian
Quantum neural network (QNN) is one of the promising directions where the near-term noisy intermediate-scale quantum (NISQ) devices could find advantageous applications against classical resources. Recurrent neural networks are the most fundamental n
Externí odkaz:
http://arxiv.org/abs/2302.03244
In this paper, we propose SinTra, an auto-regressive sequential generative model that can learn from a single multi-track music segment, to generate coherent, aesthetic, and variable polyphonic music of multi-instruments with an arbitrary length of b
Externí odkaz:
http://arxiv.org/abs/2204.09917
Due to the wavelength-dependent light attenuation, refraction and scattering, underwater images usually suffer from color distortion and blurred details. However, due to the limited number of paired underwater images with undistorted images as refere
Externí odkaz:
http://arxiv.org/abs/2201.02832
Publikováno v:
In Ocean Engineering 15 July 2024 304
Publikováno v:
In Knowledge-Based Systems 27 September 2024 300
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
We propose an image steganographic algorithm called EncryptGAN, which disguises private image communication in an open communication channel. The insight is that content transform between two very different domains (e.g., face to flower) allows one t
Externí odkaz:
http://arxiv.org/abs/1905.11582
The aim of this work is learning to reshape the object in an input image to an arbitrary new shape, by just simply providing a single reference image with an object instance in the desired shape. We propose a new Generative Adversarial Network (GAN)
Externí odkaz:
http://arxiv.org/abs/1905.06514