  Learning from a limited number of samples is challenging since the learned
model can easily become overfitted based on the biased distribution formed by
only a few training examples. In this paper, we calibrate the distribution of
these few-sample classes by transferring statistics from the classes with
sufficient examples, then an adequate number of examples can be sampled from
the calibrated distribution to expand the inputs to the classifier. We assume
every dimension in the feature representation follows a Gaussian distribution
so that the mean and the variance of the distribution can borrow from that of
similar classes whose statistics are better estimated with an adequate number
of samples. Our method can be built on top of off-the-shelf pretrained feature
extractors and classification models without extra parameters. We show that a
simple logistic regression classifier trained using the features sampled from
our calibrated distribution can outperform the state-of-the-art accuracy on two
datasets (~5% improvement on miniImageNet compared to the next best). The
visualization of these generated features demonstrates that our calibrated
distribution is an accurate estimation.
