  Transductive few-shot learning algorithms have showed substantially superior
performance over their inductive counterparts by leveraging the unlabeled
queries. However, the vast majority of such methods are evaluated on perfectly
class-balanced benchmarks. It has been shown that they undergo remarkable drop
in performance under a more realistic, imbalanced setting. To this end, we
propose a novel algorithm to address imbalanced transductive few-shot learning,
named Adaptive Manifold. Our method exploits the underlying manifold of the
labeled support examples and unlabeled queries by using manifold similarity to
predict the class probability distribution per query. It is parameterized by
one centroid per class as well as a set of graph-specific parameters that
determine the manifold. All parameters are optimized through a loss function
that can be tuned towards class-balanced or imbalanced distributions. The
manifold similarity shows substantial improvement over Euclidean distance,
especially in the 1-shot setting. Our algorithm outperforms or is on par with
other state of the art methods in three benchmark datasets, namely
miniImageNet, tieredImageNet and CUB, and three different backbones, namely
ResNet-18, WideResNet-28-10 and DenseNet-121. In certain cases, our algorithm
outperforms the previous state of the art by as much as 4.2%.
