# Few-Shot Meta-Denoising

## TL;DR

This paper introduces a meta-learning approach for few-shot denoising that improves generalization by training on synthetic noise models and fine-tuning on small real datasets, outperforming traditional methods.

## Contribution

The work proposes a novel meta-learning training method for few-shot denoising, enabling models to generalize better from limited data by leveraging synthetic noise during meta-training.

## Key findings

- Meta-learning improves denoising accuracy over supervised and transfer learning.
- The approach generalizes well to different noise types and signals.
- Meta-learning shows potential as a primary method for denoising tasks.

## Abstract

We study the problem of few-shot learning-based denoising where the training set contains just a handful of clean and noisy samples. A solution to mitigate the small training set issue is to pre-train a denoising model with small training sets containing pairs of clean and synthesized noisy signals, produced from empirical noise priors, and fine-tune on the available small training set. While such transfer learning seems effective, it may not generalize well because of the limited amount of training data. In this work, we propose a new meta-learning training approach for few-shot learning-based denoising problems. Our model is meta-trained using known synthetic noise models, and then fine-tuned with the small training set, with the real noise, as a few-shot learning task. Meta-learning from small training sets of synthetically generated data during meta-training enables us to not only generate an infinite number of training tasks, but also train a model to learn with small training sets -- both advantages have the potential to improve the generalisation of the denoising model. Our approach is empirically shown to produce more accurate denoising results than supervised learning and transfer learning in three denoising evaluations for images and 1-D signals. Interestingly, our study provides strong indications that meta-learning has the potential to become the main learning algorithm for denoising.