Logarithm-transform aided Gaussian Sampling for Few-Shot Learning
Vaibhav Ganatra
Few-shot image classification has recently witnessed the rise of
representation learning being utilised for models to adapt to new classes using
only a few training examples. Therefore, the properties of the representations,
such as their underlying probability distributions, assume vital importance.
Representations sampled from Gaussian distributions have been used in recent
works, [19] to train classifiers for few-shot classification. These methods
rely on transforming the distributions of experimental data to approximate
Gaussian distributions for their functioning. In this paper, I propose a novel
Gaussian transform, that outperforms existing methods on transforming
experimental data into Gaussian-like distributions. I then utilise this novel
transformation for few-shot image classification and show significant gains in
performance, while sampling lesser data.