Paper on scale invariance in deep learning accepted in MAKE journal

Our work “On the Scale Invariance in State of the Art CNNs Trained on ImageNet”, by Mara Graziani, Thomas Lompech, Henning Müller, Adrien Depeursinge and Vincent Andrearczyk. was published with open access rights in a special issue of the Machine Learning and Knowledge Extraction (MAKE) journal:

By using our tool Regression Concept Vectors (pip install rcvtool), we modeled information about scale within intermediate CNN layers. Scale covariance peaks at deep layers and invariance is learned only in the final layers.
Based on this, we designed a pruning strategy that preserves scale-covariant features. This gives better transfer learning results for medical tasks where scale is discriminative, for example, to distinguish the magnification level of microscope images of tissue biopses.