  We introduce Transductive Infomation Maximization (TIM) for few-shot
learning. Our method maximizes the mutual information between the query
features and their label predictions for a given few-shot task, in conjunction
with a supervision loss based on the support set. Furthermore, we propose a new
alternating-direction solver for our mutual-information loss, which
substantially speeds up transductive-inference convergence over gradient-based
optimization, while yielding similar accuracy. TIM inference is modular: it can
be used on top of any base-training feature extractor. Following standard
transductive few-shot settings, our comprehensive experiments demonstrate that
TIM outperforms state-of-the-art methods significantly across various datasets
and networks, while used on top of a fixed feature extractor trained with
simple cross-entropy on the base classes, without resorting to complex
meta-learning schemes. It consistently brings between 2% and 5% improvement in
accuracy over the best performing method, not only on all the well-established
few-shot benchmarks but also on more challenging scenarios,with domain shifts
and larger numbers of classes.
