État académique
Thèse en cours...
Sujet: Deep Learning for image recognition
Direction de thèse:
Ellipse bleue: doctorant, ellipse jaune: docteur, rectangle vert: permanent, rectangle jaune: HDR. Trait vert: encadrant de thèse, trait bleu: directeur de thèse, pointillé: jury d'évaluation à mi-parcours ou jury de thèse.
Productions scientifiques
Deep Neural Networks Under Stress
International audience
In recent years, deep architectures have been used for transfer learning with state-of-the-art performance in many data-sets. The properties of their features remain, however, largely unstudied under the transfer perspective. In this work, we present an extensive analysis of the resiliency of feature vectors extracted from deep models, with special focus on the trade-off between performance and compression rate. By introducing perturbations to image descriptions extracted from a deep convolutional neural network, we change their precision and number of dimensions, measuring how it affects the final score. We show that deep features are more robust to these disturbances when compared to classical approaches, achieving a compression rate of 98.4%, while losing only 0.88% of their original score for Pascal VOC 2007.
IEEE International Conference on Image Processing (ICIP 2016) http://hal.upmc.fr/hal-01340298 IEEE International Conference on Image Processing (ICIP 2016), Sep 2016, Phoenix, AZ, United States. <http://2016.ieeeicip.org/> http://2016.ieeeicip.org/ARRAY(0x7f03ff1831c8) 2016-09-25