OATML

High resolution remote sensing imagery is used in broad range of tasks, including detection and classification of objects. High-resolution imagery is however expensive, while lower resolution imagery is often freely available and can be used by the public for range of social good applications. To that end, we curate a multi-spectral multi-image super-resolution dataset, using PlanetScope imagery from the SpaceNet 7 challenge as the high resolution reference and multiple Sentinel-2 revisits of the same imagery as the low-resolution imagery. We present the first results of applying multi-image super-resolution (MISR) to multi-spectral remote sensing imagery. We, additionally, introduce a radiometric consistency module into MISR model the to preserve the high radiometric resolution of the Sentinel-2 sensor. We show that MISR is superior to single-image super-resolution and other baselines on a range of image fidelity metrics. Furthermore, we conduct the first assessment of the utility... [full abstract]

Training on web-scale data can take months. But much computation and time is wasted on redundant and noisy points that are already learnt or not learnable. To accelerate training, we introduce Reducible Holdout Loss Selection (RHO-LOSS), a simple but principled technique which selects approximately those points for training that most reduce the model’s generalization loss. As a result, RHO-LOSS mitigates the weaknesses of existing data selection methods: techniques from the optimization literature typically select "hard" (e.g. high loss) points, but such points are often noisy (not learnable) or less task-relevant. Conversely, curriculum learning prioritizes "easy" points, but such points need not be trained on once learned. In contrast, RHO-LOSS selects points that are learnable, worth learning, and not yet learnt. RHO-LOSS trains in far fewer steps than prior art, improves accuracy, and speeds up training on a wide range of datasets, hyperparameters, and architectures (MLPs, CNNs... [full abstract]

OATML graduate student Muhammed Razzak received a top reviewer award given to the top 8% of reviewers at NeurIPS 2022.

OATML PhD Students Sören Mindermann, Muhammed Razzak, and Jan Brauner delivered a talk to Meta AI Research on their work on prioritised training.

OATML students Pascal Notin, Andrew Jesson, Clare Lyle and Professor Yarin Gal are co-organizing the first Machine Learning for Drug Discovery (MLDD) workshop at ICLR 2022 jointly with collaborators at GSK, Harvard, MILA, MIT and others. OATML students Neil Band, Freddie Bickford Smith, Jan Brauner, Lars Holdijk, Andreas Kirsch, Jannik Kossen and Muhammed Razzak are part of the PC.

Seven papers with OATML members accepted to ICML 2021, together with 14 workshop papers. More information in our blog post.

OATML group members and collaborators are proud to present 11 papers at the ICML 2022 main conference and workshops. Group members are also co-organizing the Workshop on Computational Biology, and the Oxford Wom*n Social. …

OATML group members and collaborators are proud to present 4 papers at ICLR 2022 main conference. …

OATML group members and collaborators are proud to present 21 papers at ICML 2021, including 7 papers at the main conference and 14 papers at various workshops. Group members will also be giving invited talks and participate in panel discussions at the workshops. …

OATML group members and collaborators are proud to be presenting 22 papers at NeurIPS 2020. Group members are also co-organising various events around NeurIPS, including workshops, the NeurIPS Meet-Up on Bayesian Deep Learning and socials. …