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Alejandro Acosta
Researcher at Twitter
Publications - 6
Citations - 11969
Alejandro Acosta is an academic researcher from Twitter. The author has contributed to research in topics: Convolutional neural network & Motion compensation. The author has an hindex of 5, co-authored 6 publications receiving 9999 citations.
Papers
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Proceedings ArticleDOI
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig,Lucas Theis,Ferenc Huszar,Jose Caballero,Andrew Cunningham,Alejandro Acosta,Andrew Peter Aitken,Alykhan Tejani,Johannes Totz,Zehan Wang,Wenzhe Shi +10 more
TL;DR: SRGAN as mentioned in this paper proposes a perceptual loss function which consists of an adversarial loss and a content loss, which pushes the solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.
Posted Content
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig,Lucas Theis,Ferenc Huszar,Jose Caballero,Andrew Cunningham,Alejandro Acosta,Andrew Peter Aitken,Alykhan Tejani,Johannes Totz,Zehan Wang,Wenzhe Shi +10 more
TL;DR: SRGAN, a generative adversarial network (GAN) for image super-resolution (SR), is presented, to its knowledge, the first framework capable of inferring photo-realistic natural images for 4x upscaling factors and a perceptual loss function which consists of an adversarial loss and a content loss.
Proceedings ArticleDOI
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
Jose Caballero,Christian Ledig,Andrew Peter Aitken,Alejandro Acosta,Johannes Totz,Zehan Wang,Wenzhe Shi +6 more
TL;DR: In this article, a spatio-temporal sub-pixel convolution network is proposed to exploit temporal redundancies and improve reconstruction accuracy while maintaining real-time speed, and a novel joint motion compensation and video super-resolution algorithm that is orders of magnitude more efficient than competing methods.
Posted Content
Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks.
Joost van Amersfoort,Wenzhe Shi,Alejandro Acosta,Francisco Massa,Johannes Totz,Zehan Wang,Jose Caballero +6 more
TL;DR: A multi-scale generative adversarial network for frame interpolation (FIGAN) that is jointly supervised at different levels with a perceptual loss function that consists of an adversarial and two content losses to improve the quality of synthesised intermediate video frames.
Posted Content
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
Jose Caballero,Christian Ledig,Andrew Peter Aitken,Alejandro Acosta,Johannes Totz,Zehan Wang,Wenzhe Shi +6 more
TL;DR: A novel joint motion compensation and video super-resolution algorithm that is orders of magnitude more efficient than competing methods, relying on a fast multi-resolution spatial transformer module that is end-to-end trainable is proposed.