Difference between revisions of "Object Recognition with Deep Boltzmann Machines"

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''This page explains how the creation and modification of project pages is done. [[SMW_Project_Workflow | See here]] for the complete lifecycle of a project within the AIRWiki.''
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== Intro ==
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This project aims to exploit the ability of Deep Boltzmann machines (DBM) to classify (and generate) multiclass objects. Initially, they will be used to classify road signs with the purpose to help the navigation of an hypotetical autonomous car. Since the initial results on the classification tasks of road signs using DBM are very good, we are pretty confident about the quality of the project. After that, we will consider the possibility to extend the task to other domains.{{Project | title=Object recognition with DBMs | short_descr= Deep Boltzmann machines for classification tasks | coordinator=MatteoMatteucci | tutor=MatteoMatteucci;FrancescoVisin | students=CarloDEramo | resarea=Robotics | restopic=Machine Learning | start=2014/03/1 | level=Ms | status=Active}}
  
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== HOWTO add a new project to the AIRWiki ==
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== Deep learning and Boltzmann machines ==
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#''Become an AIRWiki user.'' To modify the wiki (including the creation of new pages), you must be one or AIRWiki's [[registered users]]. [[SMW_Project_Workflow#Student_Registration]] explains what you have to do.
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'''Deep learning''' is a set of algorithms in machine learning that attempt to model high-level abstractions in data by using architectures composed of multiple non-linear transformations.  
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#''Create a page for the project in the AIRWiki.'' Each project must have an associated page. If the project you are going to work on hasn't already one, you have to create it by following the procedure described in [[SMW_Project_Workflow#Project_Instantiation]]. It's a good idea to ask your Tutor for the name of the new page.
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We will work on Boltzmann machines, an energy-based model which has been proven to perform very well in image and speech recognition tasks. A Boltzmann machines is composed of visible units representing the inputs of the network provided by the dataset and hidden units representing the feature detectors of the net. In a general fully connected Boltzmann machine each pair of unit is connected with a symmetric connection with a certain weights. We will focus on DBMs that are a particular type of Boltzmann machines built by stacking several layers of RBM on top of each other in a proper way. A RBM is known as restricted Boltzmann machines, a Boltzmann machines with no intra-layer connections. We'll study also deep belief network (DBN), a hybrid model between a neural network with discriminative and generative connections and a DBM, which has only symmetric connections.  
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We will use ''Pylearn2'' framework which offers training procedures of deep neural networks. During the project we will go deep in the analysis of this kind of networks studying their potentiality and flexibility; we'll try different configuration of networks in order to obtain the good performances we're looking for.
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#''Describe your project'' by editing the project's page and adding information about it (see below for directions).
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As your work on the project progresses, don't forget to '''keep your project's page up-to-date''' by editing it every time you have new material. See below to learn how.
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===OK, I know what I'm doing. How do I edit a page?===
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A good starting point is to look around the AIRWiki (or Wikipedia!) and clicking 'edit' here and there to see what the source code of the pages look like (just don't save any modifications!). If you see something useful, you can copy the code and paste it into your page.
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* to have full access to the ''public layer'' of the page (including the basic information), click the 'edit' tab on the top of the page.
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Tip: when you are editing the public layer of a page SomePage, the title of the web page you are looking at is "Editing SomePage". If you are editing the private layer the title is "Editing Talk:SomePage" instead. For additional information, see [[Layers]].
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'''Important''': don't change the Semantic MediaWiki code (the lines between <nowiki>"{{" and "}}"</nowiki> on top of the wiki code) of any page if you don't know what you are doing!
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== HOWTO use a project page (guidelines) ==
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You should use your project's page to store ANY useful information about the project, as soon as it is available (results, images, data, links, decisions, roadmaps, what you have done today, descriptions of things gone well or of errors made, warnings, and pretty much everything). If such information is already sufficiently well-proven and well-formatted to be put on the public layer (accessible through the 'edit' tab), you should directly put it there. If it is not, you should put the information on the private layer of the page (accessible through the 'discussion' tab); then prepare it for publication and finally move it to the public layer as soon as possible.
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Some notes:
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* If you try to keep the contents of the private layer of your project's page reasonably well organized, the migration to the public layer will be easy.
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* It is especially useful if you include a '''project diary''' into the private layer of your project's page: a very short description of everything you do, why you did it, and what resulted from it. This will be useful to you (when writing a thesis or relation), and invaluable for those that will have to take your work further!
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* For your Tutor, the AIRWiki will be the main source of information about how your project is going, so they will look at its page (public and private parts) often and with attention ;-)
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Latest revision as of 16:09, 3 July 2014

Intro

This project aims to exploit the ability of Deep Boltzmann machines (DBM) to classify (and generate) multiclass objects. Initially, they will be used to classify road signs with the purpose to help the navigation of an hypotetical autonomous car. Since the initial results on the classification tasks of road signs using DBM are very good, we are pretty confident about the quality of the project. After that, we will consider the possibility to extend the task to other domains.

Object recognition with DBMs
Short Description: Deep Boltzmann machines for classification tasks
Coordinator: MatteoMatteucci (matteo.matteucci@polimi.it)
Tutor: MatteoMatteucci (matteo.matteucci@polimi.it), FrancescoVisin (visin@elet.polimi.it)
Collaborator:
Students: CarloDEramo (carlo.deramo@polimi.it)
Research Area: Robotics
Research Topic: Machine Learning
Start: 2014/03/1
Status: Active
Level: Ms

Deep learning and Boltzmann machines

Deep learning is a set of algorithms in machine learning that attempt to model high-level abstractions in data by using architectures composed of multiple non-linear transformations. We will work on Boltzmann machines, an energy-based model which has been proven to perform very well in image and speech recognition tasks. A Boltzmann machines is composed of visible units representing the inputs of the network provided by the dataset and hidden units representing the feature detectors of the net. In a general fully connected Boltzmann machine each pair of unit is connected with a symmetric connection with a certain weights. We will focus on DBMs that are a particular type of Boltzmann machines built by stacking several layers of RBM on top of each other in a proper way. A RBM is known as restricted Boltzmann machines, a Boltzmann machines with no intra-layer connections. We'll study also deep belief network (DBN), a hybrid model between a neural network with discriminative and generative connections and a DBM, which has only symmetric connections. We will use Pylearn2 framework which offers training procedures of deep neural networks. During the project we will go deep in the analysis of this kind of networks studying their potentiality and flexibility; we'll try different configuration of networks in order to obtain the good performances we're looking for.

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