EVOLVING OPTIMAL AUGMENTATION POLICIES FOR SELF-SUPERVISED LEARNING ALGORITHMS
Date
2023-06-28
Authors
Barrett, Noah
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Abstract
In recent years, self-supervised learning has shown remarkable promise for expanding
the capabilities of deep-learning-based computer vision models. In many self-supervised
learning approaches, specifically those that employ a Siamese Network, data augmentation is a core component of the algorithm. However, typically a standard
set of augmentations are employed without further investigation into improving the
augmentation strategy used. This thesis aims to address this issue by taking a
step forward to better understand the impact of data augmentation on cutting-edge
computer vision based self-supervised learning algorithms. Inspired by supervised
augmentation optimization approaches, this thesis explores the possibility of further
optimizing four SOTA self-supervised learning algorithms, BYOL, SwAV, NNCLR,
and SimSiam, by improving augmentation operators used in the pretext task. Using
a Genetic Algorithm, it was possible to learn augmentation policies which yielded
higher performance than the original augmentation policies for all four self-supervised
learning algorithms, on two datasets, SVHN and CIFAR-10. This thesis shows that
improving the augmentation policies used in computer vision based self-supervised
learning algorithms is a fruitful direction for further improving on the cutting-edge
performance yielded from this family of algorithms.
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Keywords
Augmentation Optimization, Self-Supervised Learning, Computer Vision, Genetic Algorithm