no code implementations • 25 Apr 2024 • Francisco Eiras, Aleksandar Petrov, Bertie Vidgen, Christian Schroeder de Witt, Fabio Pizzati, Katherine Elkins, Supratik Mukhopadhyay, Adel Bibi, Botos Csaba, Fabro Steibel, Fazl Barez, Genevieve Smith, Gianluca Guadagni, Jon Chun, Jordi Cabot, Joseph Marvin Imperial, Juan A. Nolazco-Flores, Lori Landay, Matthew Jackson, Paul Röttger, Philip H. S. Torr, Trevor Darrell, Yong Suk Lee, Jakob Foerster
In the next few years, applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education.
no code implementations • 1 Dec 2023 • Botos Csaba, Wenxuan Zhang, Matthias Müller, Ser-Nam Lim, Mohamed Elhoseiny, Philip Torr, Adel Bibi
We introduce a new continual learning framework with explicit modeling of the label delay between data and label streams over time steps.
no code implementations • 26 Sep 2022 • Botos Csaba, Adel Bibi, Yanwei Li, Philip Torr, Ser-Nam Lim
Deep learning models for vision tasks are trained on large datasets under the assumption that there exists a universal representation that can be used to make predictions for all samples.
no code implementations • 2 Aug 2021 • Botos Csaba, Xiaojuan Qi, Arslan Chaudhry, Puneet Dokania, Philip Torr
The key ingredients to our approach are -- (a) mapping the source to the target domain on pixel-level; (b) training a teacher network on the mapped source and the unannotated target domain using adversarial feature alignment; and (c) finally training a student network using the pseudo-labels obtained from the teacher.
no code implementations • 21 Feb 2019 • Botos Csaba, Adnane Boukhayma, Viveka Kulharia, András Horváth, Philip H. S. Torr
Standard adversarial training involves two agents, namely a generator and a discriminator, playing a mini-max game.