Humans teaching machines and machines teaching humans

par

Oisin Mac Aodha

Postdoc at Caltech Computational Vision Lab

Résumé:

Our current machine learning solutions are rigid (i.e. we collect, train, and deploy). In contrast, many real world problem domains are not structured in this way. We need flexible systems that enable each stakeholder (e.g. experts, annotators, and model consumers) to interact and iterate in order to efficiently reach consensus for the task at hand. This will result in the creation of living knowledge bases that are empowered by experts and available to all. To achieve this goal we need to build on ideas from active learning, machine teaching, representation learning, crowdsourcing, and interpretable machine learning.

In this talk I will discuss our recent attempts to develop methods that unite the complementary strengths of humans and machines. I will present work on the automatic teaching of visual concepts to human learners. Our proposed model provides automatically generated interpretable feedback to learners and models how they update their beliefs in light of this information. Through empirical evaluation, I will show that this results in a significant reduction in the time required to teach new concepts in varied domains such as species identification and medical diagnosis. Finally, I will also discuss the self-supervised learning of deep representations from raw unlabeled image data. I will show that rich representations that encode information about the shape and structure of the world can be extracted from image sequences without requiring any explicit supervision at training time. These types of representations offer the potential to act as a powerful initialization signal for other downstream tasks.

Biographie :

Oisin Mac Aodha is a postdoctoral scholar with Prof. Pietro Perona in the Computational Vision Lab at the California Institute of Technology (Caltech). He obtained his PhD from University College London (UCL) with Prof. Gabriel Brostow. He is a recipient of the Travelling Studentship in the Sciences from the National University of Ireland. His current research interests are broadly in the areas of machine learning, computer vision, and human-in-the-loop methods such as active learning and machine teaching. More information and a list of publications can be found on his website: www.oisin.info.