Bringing together researchers to uncover the principles of Non-euclidean representations.

Within deep learning, Euclidean geometry is the default basis for deep neural networks, yet the naive assumption that such a topology is optimal for all data types and tasks does not necessarily hold. There exists a growing body of evidence to suggest that data and the representations that we aim to learn can be better captured through learning in corresponding geometries when exhibiting non-Euclidean structures. The interest in non-Euclidean deep learning has grown dramatically in recent years, with advancing methodologies, libraries, and applications. Beyond Euclidean will be the first workshop solely devoted to deep learning in hyperbolic and hyperspherical spaces.

Hyperbolic and hyperspherical learning are quickly gaining traction in computer vision and deep learning. Hyperbolic and hyperspherical learning approaches bring new perspectives and have the ability to address open issues in conventional deep learning. Hyperbolic geometry can be viewed as the natural geometry of hierarchies (Mettes et. al. 23) and has recently shown to be a highly effective approach for problems ranging from few-shot recognition (Khrulkov et al. 20) to out-of-distribution detection (Guo et.al. 22, Flaborea et.al. 23), self-supervised learning and much more. Similarly, hyperspherical learning is omnipresent in today's contrastive learning, which powered by cosine similarities, and has deep impact in all tasks from self-supervision (Durrant et.al. 22) to long-tailed classification (Kasarla et.al. 22) and learning with limited samples (Trosten et. al. 23).

The Beyond Euclidean workshop bring together computer vision researchers with a shared interest in exploring non-Euclidean geometry. The workshop invites researchers to submit their latest work, fostering engaging discussions through invited and contributed talks. We aim to overcome the limitations of Euclidean representations and unlock new possibilities. 

Call for papers: join us to challenge conventional perspectives and shape the future of computer vision!