no code implementations • 30 Sep 2022 • Skanda Koppula, Yazhe Li, Evan Shelhamer, Andrew Jaegle, Nikhil Parthasarathy, Relja Arandjelovic, João Carreira, Olivier Hénaff
Self-supervised methods have achieved remarkable success in transfer learning, often achieving the same or better accuracy than supervised pre-training.
2 code implementations • 22 Feb 2022 • Joao Carreira, Skanda Koppula, Daniel Zoran, Adria Recasens, Catalin Ionescu, Olivier Henaff, Evan Shelhamer, Relja Arandjelovic, Matt Botvinick, Oriol Vinyals, Karen Simonyan, Andrew Zisserman, Andrew Jaegle
This however hinders them from scaling up to the inputs sizes required to process raw high-resolution images or video.
9 code implementations • 30 Oct 2018 • Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, Pushmeet Kohli
Recent work has shown that it is possible to train deep neural networks that are provably robust to norm-bounded adversarial perturbations.
no code implementations • 25 May 2018 • Krishnamurthy Dvijotham, Sven Gowal, Robert Stanforth, Relja Arandjelovic, Brendan O'Donoghue, Jonathan Uesato, Pushmeet Kohli
This paper proposes a new algorithmic framework, predictor-verifier training, to train neural networks that are verifiable, i. e., networks that provably satisfy some desired input-output properties.
no code implementations • CVPR 2015 • Akihiko Torii, Relja Arandjelovic, Josef Sivic, Masatoshi Okutomi, Tomas Pajdla
We address the problem of large-scale visual place recognition for situations where the scene undergoes a major change in appearance, for example, due to illumination (day/night), change of seasons, aging, or structural modifications over time such as buildings built or destroyed.
no code implementations • CVPR 2013 • Relja Arandjelovic, Andrew Zisserman
The objective of this paper is large scale object instance retrieval, given a query image.