Logarithm-transform aided Gaussian Sampling for Few-Shot Learning

• URL: http://arxiv.org/abs/2309.16337v1
• Date: Thu, 28 Sep 2023 10:50:32 GMT
• Title: Logarithm-transform aided Gaussian Sampling for Few-Shot Learning
• Authors: Vaibhav Ganatra
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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