Logarithm-transform aided Gaussian Sampling for Few-Shot Learning

Vaibhav Ganatra

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arXiv · 2309.16337

Logarithm-transform aided Gaussian Sampling for Few-Shot Learning

Vaibhav Ganatra

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Abstract

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.

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Vaibhav Ganatra. 2023-09-28. Logarithm-transform aided Gaussian Sampling for Few-Shot Learning. https://arxiv.org/abs/2309.16337

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