1 code implementation • 26 Oct 2023 • Karsten Roth, Lukas Thede, Almut Sophia Koepke, Oriol Vinyals, Olivier Hénaff, Zeynep Akata
Training deep networks requires various design decisions regarding for instance their architecture, data augmentation, or optimization.
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.
7 code implementations • ICLR 2022 • Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, Joāo Carreira
A central goal of machine learning is the development of systems that can solve many problems in as many data domains as possible.
Ranked #1 on Optical Flow Estimation on KITTI 2015 (Average End-Point Error metric)