Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness

By Magdalini Paschali, Walter Simson, Abhijit Guha Roy, Muhammad Ferjad Naeem, Rüdiger Göbl, Christian Wachinger, Nassir Navab. 2019

22 citations recorded by the source corpus.

In this paper we propose a novel augmentation technique that improves not only the performance of deep neural networks on clean test data, but also significantly increases their robustness to random transformations, both affine and projective. Inspired by ManiFool, the augmentation is performed by a line-search manifold-exploration method that learns affine geometric transformations that lead to the misclassification on an image, while ensuring that it remains on the same manifold as the training data. This augmentation method populates any training dataset with images that lie on the border of the manifolds between two-classes and maximizes the variance the network is exposed to during training. Our method was thoroughly evaluated on the challenging tasks of fine-grained skin lesion classification from limited data, and breast tumor classification of mammograms. Compared with traditional augmentation methods, and with images synthesized by Generative Adversarial Networks our method not only achieves state-of-the-art performance but also significantly improves the network's robustness.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

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Citations in the corpus

A Survey of Data Augmentation Approaches for NLP (2021, methodology): “Paschali et al. (2019) use iterative afﬁne transformations and projections to maximally "stretch" an example along the class-manifold.”
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules (2019, methodology): “These use methods such as manifold learning (Paschali et al., 2019), Bayesian Optimization (Tran et al., 2017), and generative adversarial networks which generate transformation sequences (Ratner et al., 2017).”
Fast AutoAugment (2019, methodology): “Notably, applying a carefully designed set of augmentations rather than naive random transformations in training improves the generalization ability of a network significantly [21, 26].”
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness (2020, background): “…assumption to improve robustness by moving examples towards a decision boundary (Kanbak et al., 2018), generating adversarial examples (Szegedy et al., 2014; Miyato et al., 2017), interpolating between pairs of examples (Zhang et al., 2018), or ﬁnding afﬁne transforms (Paschali et al., 2019).”