# Occlusions for Effective Data Augmentation in Image Classification

Source: [Occlusions for Effective Data Augmentation in Image Classification](<https://www.sota2.com/research/paper/68fa668f1e209131cea10d57>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:52:21.192Z

Authors: Ruth Fong, Andrea Vedaldi

Published: 2019-10-23T16:19:22.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1910.10651>)

[PDF](<https://arxiv.org/pdf/1910.10651.pdf>)