[Lisa_seminaires] Soutenance de thèse - PhD defense - David Warde-Farley

Yoshua Bengio yoshua.umontreal at gmail.com
Mar 5 Déc 20:03:36 EST 2017


Double feature next Monday - two PhD defenses: first David Warde-Farley,
then Li Yao

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Soutenance de thèse / PhD defense

Lundi 11 décembre, local 3195 du Pavillon Aisenstadt
13h-15h

Candidat: David Warde-Farley
Directeur: Yoshua Bengio
Président du jury: Emma Frejinger
Examinateur externe: Graham Taylor
Membre du jury: Simon Lacoste-Julien


Feedforward architectures for classification and synthesis


This thesis by articles makes several contributions to the field of deep
learning, with applications to both classification and synthesis of natural
images. Specifically, we introduce several new techniques for the
construction and training of deep feed- forward networks, and present an
empirical investigation into dropout, one of the most popular
regularization strategies of the last several years.

In the first article, we present a novel piece-wise linear parameterization
of neural networks, maxout, which allows each hidden unit of a neural
network to effectively learn its own convex activation function. We
demonstrate improvements on several object recognition benchmarks, and
empirically investigate the source of these improvements, including an
improved synergy with the recently proposed dropout regularization method.

In the second article, we further interrogate the dropout algorithm in
particular. Focusing on networks of the popular rectified linear units
(ReLU), we empirically examine several questions regarding dropout’s
remarkable effectiveness as a regularizer, including questions surrounding
the fast test-time rescaling trick and the geometric mean it approximates,
interpretations as an ensemble as compared with traditional ensembles, and
the importance of using a bagging-like criterion for optimization.

In the third article, we address a practical problem in industrial-scale
application of deep networks for multi-label object recognition, namely
improving an existing model’s ability to discriminate between frequently
confused classes. We accomplish this by using the network’s own predictions
to inform a partitioning of the label space, and augment the network with
dedicated discriminative capacity addressing each of the partitions.

Finally, in the fourth article, we tackle the problem of fitting implicit
generative models of open domain collections of natural images using the
recently introduced Generative Adversarial Networks (GAN) paradigm. We
introduce an augmented training procedure which employs a denoising
autoencoder, trained in a high-level feature space learned by the
discriminator, to guide the generator towards feature encodings which more
closely resemble the data. We quantitatively evaluate our findings using
the recently proposed Inception score.
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