Within modern Deep Learning setups, data augmentation is the weapon of choice when dealing with narrow datasets or with a poor range of different samples. However, the benefits of data augmentation are abysmal when applied to a dataset which is inherently unable to cover all the categories to be classified with a significant number of samples. To deal with such desperate scenarios, we propose a possible last resort: Cross-Dataset Data Augmentation. That is, the creation of new samples by morphing observations from a different source into credible specimens for the training dataset. Of course specific and strict conditions must be satisfied for this trick to work. In this paper we propose a general set of strategies and rules for Cross-Dataset Data Augmentation and we demonstrate its feasibility over a concrete case study. Even without defining any new formal approach, we think that the preliminary results of our paper are worth to produce a broader discussion on this topic.
Gasparetto, Andrea (Corresponding)
|Data di pubblicazione:||2018|
|Titolo:||Cross-Dataset Data Augmentation for Convolutional Neural Networks Training|
|Rivista:||INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION|
|Titolo del libro:||Proceedings - International Conference on Pattern Recognition|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/ICPR.2018.8545812|
|Appare nelle tipologie:||4.1 Articolo in Atti di convegno|