  We introduce Mixture-based Feature Space Learning (MixtFSL) for obtaining a
rich and robust feature representation in the context of few-shot image
classification. Previous works have proposed to model each base class either
with a single point or with a mixture model by relying on offline clustering
algorithms. In contrast, we propose to model base classes with mixture models
by simultaneously training the feature extractor and learning the mixture model
parameters in an online manner. This results in a richer and more
discriminative feature space which can be employed to classify novel examples
from very few samples. Two main stages are proposed to train the MixtFSL model.
First, the multimodal mixtures for each base class and the feature extractor
parameters are learned using a combination of two loss functions. Second, the
resulting network and mixture models are progressively refined through a
leader-follower learning procedure, which uses the current estimate as a
"target" network. This target network is used to make a consistent assignment
of instances to mixture components, which increases performance and stabilizes
training. The effectiveness of our end-to-end feature space learning approach
is demonstrated with extensive experiments on four standard datasets and four
backbones. Notably, we demonstrate that when we combine our robust
representation with recent alignment-based approaches, we achieve new
state-of-the-art results in the inductive setting, with an absolute accuracy
for 5-shot classification of 82.45 on miniImageNet, 88.20 with tieredImageNet,
and 60.70 in FC100 using the ResNet-12 backbone.
