[Lisa_seminaires] Christian Gagné and Jean-François Lalonde from U. of Laval.

Junyoung Chung elecegg at gmail.com
Lun 29 Mai 18:58:53 EDT 2017


Hi all,

we have two tea-talks (each is a 30-min talk) this week!
Both speakers are from Univ. of Laval.
You will find more information below.


Talk by Christian Gagné

Time: 13:45~14:15

Title:
Alternating Direction Method of Multipliers for Sparse Convolutional Neural
Networks

Abstract:
The storage and computation requirements of Convolutional Neural Networks
(CNNs) can be prohibitive for exploiting these models over low-power or
embedded devices. We are proposing to reduce the computational complexity
of the CNNs by minimizing an objective function, including the recognition
loss that is augmented with a sparsity-promoting penalty term. The sparsity
structure of the network is identified using the Alternating Direction
Method of Multipliers (ADMM), which is widely used in large optimization
problems. This method alternates between promoting the sparsity of the
network and optimizing the recognition performance, which allows us to
exploit the two-part structure of the corresponding objective functions. In
particular, we take advantage of the separability of the sparsity-inducing
penalty functions to decompose the minimization problem into sub-problems
that can be solved sequentially. Applying our method to a variety of
state-of-the-art CNN models, our proposed method is able to simplify the
original model, generating models with less computation and fewer
parameters, while maintaining and often improving generalization
performance. Accomplishments on a variety of models strongly verify that
our proposed ADMM-based method can be a very useful tool for simplifying
and improving deep CNNs.

Short bio:
Christian Gagné is professor at the Electrical Engineering and Computer
Engineering Department of Université Laval since 2008. He is member of the
Computer Vision and Systems Laboratory, the REPARTI FRQNT Strategic
Cluster, and the Big Data Research Centre. He completed a Ph.D. in
Electrical Engineering (Université Laval) in 2005 before being postdoctoral
fellow at the INRIA Saclay - Île-de-France (France) and the University of
Lausanne (Switzerland) in 2005-2006. He was research associate for
Informatique WGZ Inc. (2006-2007) and MacDonald, Dettwiler, and Associates
Ltd. (2007-2008). His research interests are on the engineering of
intelligent systems, especially systems based on machine learning,
evolutionary computing, and their application to pattern recognition, smart
sensors, smart grids, and simulation of public health processes.



Talk by Jean-François Lalonde

Time: 14:15~14:45

Title:
Learning to Predict Illumination from a Single Image

Abstract:
We propose an automatic method to infer high dynamic range illumination
from a single, limited field-of-view, low dynamic range photograph of a
scene. In contrast to previous work that relies on specialized image
capture, user input, and/or simple scene models, we train an end-to-end
deep neural network that directly regresses a limited field-of-view photo
to HDR illumination, without strong assumptions on scene geometry, material
properties, or lighting. This allows us to automatically recover
high-quality HDR illumination estimates that significantly outperform
previous state-of-the-art methods. Consequently, using our illumination
estimates for applications like 3D object insertion, we can achieve results
that are photo-realistic.

Bio:
Jean-François Lalonde is an Assistant Professor in Electrical and Computer
Engineering at Laval University, Quebec City, since 2013. Previously, he
was a Post-Doctoral Associate at Disney Research, Pittsburgh. He received a
B.Eng. degree in Computer Engineering with honors from Laval University,
Canada, in 2004. He earned his M.S at the Robotics Institute at Carnegie
Mellon University in 2006 under Prof. Martial Hebert and received his
Ph.D., also from Carnegie Mellon, in 2011 under the supervision of Profs.
Alexei A. Efros and Srinivasa G. Narasimhan. His thesis, titled
"Understanding and Recreating Appearance under Natural Illumination," won
the 2010-11 CMU School of Computer Science Distinguished Dissertation
Award. After graduation, he became a Computer Vision Scientist at Tandent,
Inc., where he helped develop LightBrush™, the first commercial intrinsic
imaging application. He also introduced intrinsic videos at SIGGRAPH
2012 while at Tandent. His research focuses on lighting-aware image
understanding and synthesis by leveraging large amounts of data. More at
www.jflalonde.ca.

Best,
--Junyoung
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