Sponsored by


Jose Alvarez

Call for Papers

Download CfP



Description of the workshop

Most of the major advances in Deep Learning have come from supervised learning. Despite these successes, supervised learning algorithms are characterized by a major limitation: they necessitate massive amounts of carefully, and typically expensively, annotated data. This workshop will emphasis future directions beyond supervised learning such as reinforcement learning and weakly supervised learning. Such approaches require far less supervision and allow computers to learn beyond mimicking what is explicitly encoded in a large-scale set of annotations. We encourage researchers to formulate innovative learning theories, feature representations, and end-to-end vision systems based on deep learning. We also encourage new theories and processes for dealing with large scale image datasets through deep learning architectures. We are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to:

As main difference with previous years, for this edition of the workshop, papers are meant to be extended abstracts showing current / preliminary / novel results to encourage discussion during the workshop.

Invited Speakers

Paper Submission

Important Dates

  1. Paper Submission: March 25th, 2018
  2. Supplemental Material Submission: March 31st, 2018
  3. Author Notification: April 10th, 2018
  4. Camera Ready: May 15th, 2017

Organizing Commitee