  We introduce a simple non-linear embedding adaptation layer, which is
fine-tuned on top of fixed pre-trained features for one-shot tasks, improving
significantly transductive entropy-based inference for low-shot regimes. Our
norm-induced transformation could be understood as a re-parametrization of the
feature space to disentangle the representations of different classes in a task
specific manner. It focuses on the relevant feature dimensions while hindering
the effects of non-relevant dimensions that may cause overfitting in a one-shot
setting. We also provide an interpretation of our proposed feature
transformation in the basic case of few-shot inference with K-means clustering.
Furthermore, we give an interesting bound-optimization link between K-means and
entropy minimization. This emphasizes why our feature transformation is useful
in the context of entropy minimization. We report comprehensive experiments,
which show consistent improvements over a variety of one-shot benchmarks,
outperforming recent state-of-the-art methods.
