Saliency-based Regularization of Convolutional Neural Networks

  • Deli Bajraktari

Student thesis: Master typesMaster en ingénieur de gestion à finalité spécialisée en data science


Thanks to the growth of computing resources, deep learning is used in a multitude of fields (computer vision, speech recognition, etc.) to perform all kinds of tasks (classification, prediction, text generation, etc.). However, its application is often compared to a ”black box” where it is difficult to see how a certain task is performed. This work introduces the basic concepts of deep learning and presents different architectures of deep neural net- works. It describes existing techniques implemented to visualize the parts of an image that a model considers (saliency maps) to achieve a certain task (e.g. prediction of the class of an image). This work introduces a new approach: creating a new regularization term called saliency-based regularization whose goal is to encourage the use of relevant parts of the image in order to make the prediction of a model more consistent. It also develops a method to retrieve the visualization of each neuron composing the penultimate layer of a model. These visualizations are used to compute the saliency-based regularization. The results of using this new regularization technique to train a model are encouraging and it opens the door to a new way of training a model using saliency.
la date de réponse26 août 2020
langue originaleAnglais
L'institution diplômante
  • Universite de Namur
SuperviseurBenoît Frénay (Jury)

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