211 citations · 474 across the 5 of their papers we have counts for
7 papers
Smile, Be Happy :) Emoji Embedding for Visual Sentiment Analysis
Ziad Al-Halah, Andrew Aitken, Wenzhe Shi +1
Due to the lack of large-scale datasets, the prevailing approach in visual sentiment analysis is to leverage models trained for object classification in large datasets like ImageNe…
Deep Hashing using Entropy Regularised Product Quantisation Network
Jo Schlemper, Jose Caballero, Andy Aitken +1
In large scale systems, approximate nearest neighbour search is a crucial algorithm to enable efficient data retrievals. Recently, deep learning-based hashing algorithms have been…
Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize
Andrew Aitken, Christian Ledig, Lucas Theis +3
The most prominent problem associated with the deconvolution layer is the presence of checkerboard artifacts in output images and dense labels. To combat this problem, smoothness c…
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
Jose Caballero, Christian Ledig, Andrew Aitken +4
Convolutional neural networks have enabled accurate image super-resolution in real-time. However, recent attempts to benefit from temporal correlations in video super-resolution ha…
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Wenzhe Shi, Jose Caballero, Ferenc Huszár +5
Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-res…
Is the deconvolution layer the same as a convolutional layer?
Wenzhe Shi, Jose Caballero, Lucas Theis +4
In this note, we want to focus on aspects related to two questions most people asked us at CVPR about the network we presented. Firstly, What is the relationship between our propos…
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig, Lucas Theis, Ferenc Huszar +8
Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved…