[Lisa_teatalk] Tea talk Thu 13th Feb, 14h-15h in Z300

Razvan Pascanu r.pascanu at gmail.com
Tue Feb 11 08:40:10 EST 2014


Sorry for spamming but the title of the email might have been a bit
confusing.

To emphasize:  Tea talk is **Thursday 13th Feb**.

Razvan



On Tue, Feb 11, 2014 at 8:28 AM, Razvan Pascanu <r.pascanu at gmail.com> wrote:

> Hi all,
>
> Thursday we have a tea talk by Jyri Kivinen. The room is going to be Z300
> McNicoll pavilion, time 14h00 to 15h00. PLEASE NOTE THE CHANGE OF TIME AND
> PLACE.
>
> Here is the title and abstract:
>
> Title:
> Statistical Models for Natural Scene Data
>
> Abstract:
> This talk describes work largely described in my PhD thesis (University of
> Edinburgh, 2013) titled Statistical Models for Natural Scene Data.
>
> We consider statistical modelling of natural image data. Obtaining
> advances in this field can have significant impact for both engineering
> applications, and for the understanding of the human visual system. Several
> recent advances in natural image modeling have been obtained with the use
> of unsupervised feature learning. We consider a class of such models,
> restricted Boltzmann machines (RBMs), used in many recent state-of-the-art
> image models. We develop extensions of these stochastic artificial neural
> networks, and use them as a basis for building more effective image models,
> and tools for computational vision.
>
> First, motivated by the unsatisfactory results of current generative
> natural image models, we take a step back, and evaluate whether they are
> able to model a subclass of the data, natural image textures. This is a
> necessary subcomponent of any credible model for visual scenes. We assess
> the performance of a state-of-the-art model of natural images for texture
> generation, using a dataset and evaluation techniques from in prior work.
> We also perform a dissection of the model architecture, uncovering the
> properties important for good performance.  Building on this, we develop
> structured extensions for more complicated data comprised of textures from
> multiple classes, using the single-texture
> model architecture as a basis. These models are shown to be able to
> produce state-of-the-art texture synthesis results quantitatively, and are
> also effective qualitatively. It is demonstrated empirically that the
> developed multiple-texture framework provides a means to generate images of
> differently textured regions, more generic globally varying textures, and
> can also be used for texture interpolation, where the approach is radically
> different from the others in the area.
>
> We then consider visual boundary prediction from natural images. The work
> aims to improve understanding of Boltzmann machines in the generation of
> image segment boundaries, and to investigate deep neural network
> architectures for learning the boundary detection problem. The developed
> networks (which avoid several hand-crafted model and feature designs
> commonly used for the problem), produce effective inference with
> state-of-the-art or better performance.
>
> Finally we discuss a novel framework we have developed for obtaining
> Boltzmann machines, in which the hidden unit activations co-transform with
> transformed input stimuli in a stable and predictable way throughout the
> network. We define such models to be \emph{transformation equivariant}.
> Such properties have been shown useful for computer vision systems, and
> have been motivational for example in the development of steerable filters,
> a widely used classical feature extraction technique. Translation
> equivariant feature sharing has been the standard method for scaling image
> models beyond patch-sized data to large images. In our framework we extend
> shallow and deep models to account for other kinds of transformations as
> well, focusing on in-plane rotations.
>
> I hope to see many of you there !
>
> Best,
> Razvan
>
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