  Autonomous agents interacting with the real world need to learn new concepts
efficiently and reliably. This requires learning in a low-data regime, which is
a highly challenging problem. We address this task by introducing a fast
optimization-based meta-learning method for few-shot classification. It
consists of an embedding network, providing a general representation of the
image, and a base learner module. The latter learns a linear classifier during
the inference through an unrolled optimization procedure. We design an inner
learning objective composed of (i) a robust classification loss on the support
set and (ii) an entropy loss, allowing transductive learning from unlabeled
query samples. By employing an efficient initialization module and a Steepest
Descent based optimization algorithm, our base learner predicts a powerful
classifier within only a few iterations. Further, our strategy enables
important aspects of the base learner objective to be learned during
meta-training. To the best of our knowledge, this work is the first to
integrate both induction and transduction into the base learner in an
optimization-based meta-learning framework. We perform a comprehensive
experimental analysis, demonstrating the speed and effectiveness of our
approach on four few-shot classification datasets. The Code is available at
\href{https://github.com/4rdhendu/FIML}{\textcolor{blue}{https://github.com/4rdhendu/FIML}}.
