1 code implementation • 16 May 2023 • Ameya Prabhu, Zhipeng Cai, Puneet Dokania, Philip Torr, Vladlen Koltun, Ozan Sener
In this paper, we target such applications, investigating the online continual learning problem under relaxed storage constraints and limited computational budgets.
1 code implementation • CVPR 2023 • Ameya Prabhu, Hasan Abed Al Kader Hammoud, Puneet Dokania, Philip H. S. Torr, Ser-Nam Lim, Bernard Ghanem, Adel Bibi
Our conclusions are consistent in a different number of stream time steps, e. g., 20 to 200, and under several computational budgets.
no code implementations • NeurIPS 2021 • Keyu Tian, Chen Lin, Ser Nam Lim, Wanli Ouyang, Puneet Dokania, Philip Torr
Automated data augmentation (ADA) techniques have played an important role in boosting the performance of deep models.
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.
2 code implementations • 14 May 2020 • Christian Schroeder de Witt, Bradley Gram-Hansen, Nantas Nardelli, Andrew Gambardella, Rob Zinkov, Puneet Dokania, N. Siddharth, Ana Belen Espinosa-Gonzalez, Ara Darzi, Philip Torr, Atılım Güneş Baydin
The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision makers about suitable prevention and containment strategies.
no code implementations • 25 Sep 2019 • Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr, Puneet Dokania
When combined with temperature scaling, focal loss, whilst preserving accuracy and yielding state-of-the-art calibrated models, also preserves the confidence of the model's correct predictions, which is extremely desirable for downstream tasks.