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

Edouard Oyallon (1, 2, 3) , Eugene Belilovsky (4) , Sergey Zagoruyko (5) , Michal Valko (2)

1 CVN - Centre de vision numérique
2 SEQUEL - Sequential Learning
3 GALEN-POST - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
4 DIRO - Département d'Informatique et de Recherche Opérationnelle [Montreal]
5 WILLOW - Models of visual object recognition and scene understanding

We study the first-order scattering transform as a candidate for reducing the signal processed by a convolutional neural network (CNN). We study this transformation and 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 show 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 deliver substantial improvements in the inference speed and training memory consumption compared to models trained directly on the input image.

Mots clés: First-order scattering; SIFT; CNN; Image descriptors

Domaines: Machine Learning [stat.ML]

Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko, Michal Valko. Compressing the Input for CNNs with the First-Order Scattering Transform. ECCV 2018 - European Conference on Computer Vision, Sep 2018, Munich, Germany. ⟨hal-01850921⟩