Learning to Support: Exploiting Structure Information in Support Sets for One-Shot Learning

Understand

Deep Learning shows very good performance when trained on large labeled data sets.

• The problem of training a deep net on a few or one sample per class requires a different learning approach which can generalize to unseen classes using only a few representatives of these classes.
• This problem has previously been approached by meta-learning.
• Here we propose a novel meta-learner which shows state-of-the-art performance on common benchmarks for one/few shot classification.