Context Augmentation for Convolutional Neural Networks
By Aysegul Dundar, Ignacio Garcia-Dorado. 2017
5 citations recorded by the source corpus.
Recent enhancements of deep convolutional neural networks (ConvNets) empowered by enormous amounts of labeled data have closed the gap with human performance for many object recognition tasks. These impressive results have generated interest in understanding and visualization of ConvNets. In this work, we study the effect of background in the task of image classification. Our results show that changing the backgrounds of the training datasets can have drastic effects on testing accuracies. Furthermore, we enhance existing augmentation techniques with the foreground segmented objects. The findings of this work are important in increasing the accuracies when only a small dataset is available, in creating datasets, and creating synthetic images.
Subfields: Computer Vision and Pattern Recognition
How researchers see this paper
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In Computer Vision and Pattern Recognition
- Cynthia Rudin
- Chaofan Tao
- Joseph Y. Lo
- Alina Jade Barnett
- Fides Regina Schwartz
- Yinhao Ren
- Christin Seifert
- Meike Nauta
Citations in the corpus
- A case-based interpretable deep learning model for classification of mass lesions in digital mammography (2021, background): “The ability of these networks to use context or background information is so powerful that networks trained on images of only the background outperform networks trained on images of only the object to be classified [16, 56].”