This paper focuses on variables affecting L1 transfer in L2 acquisition, which, according to the author, are categorized into three groups： learner-related variables, language-based variables and socio-linguistic var...This paper focuses on variables affecting L1 transfer in L2 acquisition, which, according to the author, are categorized into three groups： learner-related variables, language-based variables and socio-linguistic variables, and each of them is clarified in more details.展开更多
Significant progress has been made in image inpainting methods in recent years.However,they are incapable of producing inpainting results with reasonable structures,rich detail,and sharpness at the same time.In this p...Significant progress has been made in image inpainting methods in recent years.However,they are incapable of producing inpainting results with reasonable structures,rich detail,and sharpness at the same time.In this paper,we propose the Pyramid-VAE-GAN network for image inpainting to address this limitation.Our network is built on a variational autoencoder(VAE)backbone that encodes high-level latent variables to represent complicated high-dimensional prior distributions of images.The prior assists in reconstructing reasonable structures when inpainting.We also adopt a pyramid structure in our model to maintain rich detail in low-level latent variables.To avoid the usual incompatibility of requiring both reasonable structures and rich detail,we propose a novel cross-layer latent variable transfer module.This transfers information about long-range structures contained in high-level latent variables to low-level latent variables representing more detailed information.We further use adversarial training to select the most reasonable results and to improve the sharpness of the images.Extensive experimental results on multiple datasets demonstrate the superiority of our method.Our code is available at https://github.com/thy960112/Pyramid-VAE-GAN.展开更多
文摘This paper focuses on variables affecting L1 transfer in L2 acquisition, which, according to the author, are categorized into three groups： learner-related variables, language-based variables and socio-linguistic variables, and each of them is clarified in more details.
基金The authors gratefully acknowledge the financial support of the National Natural Science Foundation of China(Grant No.61925603).
文摘Significant progress has been made in image inpainting methods in recent years.However,they are incapable of producing inpainting results with reasonable structures,rich detail,and sharpness at the same time.In this paper,we propose the Pyramid-VAE-GAN network for image inpainting to address this limitation.Our network is built on a variational autoencoder(VAE)backbone that encodes high-level latent variables to represent complicated high-dimensional prior distributions of images.The prior assists in reconstructing reasonable structures when inpainting.We also adopt a pyramid structure in our model to maintain rich detail in low-level latent variables.To avoid the usual incompatibility of requiring both reasonable structures and rich detail,we propose a novel cross-layer latent variable transfer module.This transfers information about long-range structures contained in high-level latent variables to low-level latent variables representing more detailed information.We further use adversarial training to select the most reasonable results and to improve the sharpness of the images.Extensive experimental results on multiple datasets demonstrate the superiority of our method.Our code is available at https://github.com/thy960112/Pyramid-VAE-GAN.
