  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.

Computer Vision and Pattern Recognition;Machine Learning