39 code implementations • CVPR 2016 • Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, Zehan Wang
This means that the super-resolution (SR) operation is performed in HR space.
Ranked #1 on Video Super-Resolution on Xiph HD - 4x upscaling
4 code implementations • 1 Mar 2017 • Lucas Theis, Wenzhe Shi, Andrew Cunningham, Ferenc Huszár
We propose a new approach to the problem of optimizing autoencoders for lossy image compression.
2 code implementations • 24 Dec 2011 • Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, Máté Lengyel
Information theoretic active learning has been widely studied for probabilistic models.
3 code implementations • NeurIPS 2018 • Iryna Korshunova, Jonas Degrave, Ferenc Huszár, Yarin Gal, Arthur Gretton, Joni Dambre
We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations.
2 code implementations • Twitter 2018 • Lucas Theis, Iryna Korshunova, Alykhan Tejani, Ferenc Huszár
Predicting human fixations from images has recently seen large improvements by leveraging deep representations which were pretrained for object recognition.
1 code implementation • 7 Apr 2012 • Ferenc Huszár, David Duvenaud
We show that the criterion minimised when selecting samples in kernel herding is equivalent to the posterior variance in Bayesian quadrature.
1 code implementation • 16 Nov 2015 • Ferenc Huszár
We introduce a generalisation of adversarial training, and show how such method can interpolate between maximum likelihood training and our ideal training objective.
1 code implementation • 15 Dec 2022 • Royson Lee, Rui Li, Stylianos I. Venieris, Timothy Hospedales, Ferenc Huszár, Nicholas D. Lane
Recent image degradation estimation methods have enabled single-image super-resolution (SR) approaches to better upsample real-world images.
no code implementations • 11 Dec 2017 • Ferenc Huszár
Elastic weight consolidation (EWC, Kirkpatrick et al, 2017) is a novel algorithm designed to safeguard against catastrophic forgetting in neural networks.
no code implementations • 27 Feb 2017 • Ferenc Huszár
Generative adversarial networks (GANs) have given us a great tool to fit implicit generative models to data.
no code implementations • 14 Oct 2016 • Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, Ferenc Huszár
We show that, using this architecture, the amortised MAP inference problem reduces to minimising the cross-entropy between two distributions, similar to training generative models.
no code implementations • 22 Nov 2021 • Anna Kerekes, Anna Mészáros, Ferenc Huszár
In gradient descent, changing how we parametrize the model can lead to drastically different optimization trajectories, giving rise to a surprising range of meaningful inductive biases: identifying sparse classifiers or reconstructing low-rank matrices without explicit regularization.
no code implementations • 19 Oct 2022 • Szilvia Ujváry, Zsigmond Telek, Anna Kerekes, Anna Mészáros, Ferenc Huszár
Sharpness-aware minimization (SAM) aims to improve the generalisation of gradient-based learning by seeking out flat minima.