@inproceedings{d38a26ddcf3f415383eeca87d79fa192,
title = "Occlusions for effective data augmentation in image classification",
abstract = "Deep networks for visual recognition are known to leverage ''easy to recognise'' portions of objects such as faces and distinctive texture patterns. The lack of a holistic understanding of objects may increase fragility and overfitting. In recent years, several papers have proposed to address this issue by means of occlusions as a form of data augmentation. However, successes have been limited to tasks such as weak localization and model interpretation, but no benefit was demonstrated on image classification on large-scale datasets. In this paper, we show that, by using a simple technique based on batch augmentation, occlusions as data augmentation can result in better performance on ImageNet for high-capacity models (e.g., ResNet50). We also show that varying amounts of occlusions used during training can be used to study the robustness of different neural network architectures.",
keywords = "Classification, Computer-vision, Deep-learning, Explainability, Interpretability",
author = "Ruth Fong and Andrea Vedaldi",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 ; Conference date: 27-10-2019 Through 28-10-2019",
year = "2019",
month = oct,
doi = "10.1109/ICCVW.2019.00511",
language = "English (US)",
series = "Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4158--4166",
booktitle = "Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019",
address = "United States",
}