  Few-shot classification aims to adapt classifiers to novel classes with a few
training samples. However, the insufficiency of training data may cause a
biased estimation of feature distribution in a certain class. To alleviate this
problem, we present a simple yet effective feature rectification method by
exploring the category correlation between novel and base classes as the prior
knowledge. We explicitly capture such correlation by mapping features into a
latent vector with dimension matching the number of base classes, treating it
as the logarithm probability of the feature over base classes. Based on this
latent vector, the rectified feature is directly constructed by a decoder,
which we expect maintaining category-related information while removing other
stochastic factors, and consequently being closer to its class centroid.
Furthermore, by changing the temperature value in softmax, we can re-balance
the feature rectification and reconstruction for better performance. Our method
is generic, flexible and agnostic to any feature extractor and classifier,
readily to be embedded into existing FSL approaches. Experiments verify that
our method is capable of rectifying biased features, especially when the
feature is far from the class centroid. The proposed approach consistently
obtains considerable performance gains on three widely used benchmarks,
evaluated with different backbones and classifiers.
  The code will be made public.
