Compressing the Input for CNNs with the First-Order Scattering Transform

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Authors Eugene Belilovsky, Sergey Zagoruyko, Edouard Oyallon, Michal Valko
Journal/Conference Name ECCV 2018 9
Paper Category
Paper Abstract We study the first-order scattering transform as a candidate for reducing the signal processed by a convolutional neural network (CNN). We show theoretical and empirical evidence that in the case of natural images and sufficiently small translation invariance, this transform preserves most of the signal information needed for classification while substantially reducing the spatial resolution and total signal size. We demonstrate that cascading a CNN with this representation performs on par with ImageNet classification models, commonly used in downstream tasks, such as the ResNet-50. We subsequently apply our trained hybrid ImageNet model as a base model on a detection system, which has typically larger image inputs. On Pascal VOC and COCO detection tasks we demonstrate improvements in the inference speed and training memory consumption compared to models trained directly on the input image.
Date of publication 2018
Code Programming Language Python
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