Few-Shot Learning: Thoughts On Where We Should Be Going
Few-shot learning is the problem of learning new tasks from little amounts of labeled data. This is achieved by performing a form of transfer learning, from the data of many other existing tasks. This topic has gained tremendous interest in the past few years, with several new methods being proposed each month. In this talk, I suggest we take a step back, look at what we have achieved and, most importantly, consider where this research should be going next.
Hugo Larochelle is Research Scientist at Google and Assistant Professor at the Université de Sherbrooke (UdeS). Before, he was working with Twitter and he also spent two years in the machine learning group at University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton. He obtained his Ph.D. at Université de Montréal, under the supervision of Yoshua Bengio. He is the recipient of two Google Faculty Awards. His professional involvement includes associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), member of the editorial board of the Journal of Artificial Intelligence Research (JAIR) and program chair for the International Conference on Learning Representations (ICLR) of 2015 and 2016.