Difference between revisions of "Object Recognition with Deep Boltzmann Machines"
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== Deep learning and Deep Belief Network == | == Deep learning and Deep Belief Network == | ||
| − | 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. | + | '''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. |
| − | A deep belief network (DBN) is a generative graphical model, or alternatively a type of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. More precisely, each layer acts as a feature detector on inputs and serves as the visible layer for the next. | + | A '''deep belief network''' (DBN) is used for deep learning tasks, it is a generative graphical model, or alternatively a type of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. More precisely, each layer acts as a ''feature detector'' on inputs and serves as the visible layer for the next. In this way it is possible to have a fast unsupervised training procedure which will be very useful for the purpose of our project due to the complexity of the domain. |
| + | 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 network studying their potentiality and flexibility; we'll try different configuration of network in order to obtain the good performances we're looking for. | ||
Revision as of 00:21, 28 February 2014
Intro
This project aims to exploit the ability of Deep Belief Network (DBN) to classify (and generate) multiclass objects. Initially, they will be used to classify road signs with the purpose help the navigation of an hypotetical autonomous car. Since the initial results on the classification tasks of road signs using DBN are very good, we are pretty confident about the quality of the project.
DBN
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| Short Description: | DBN 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 Deep Belief Network
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. A deep belief network (DBN) is used for deep learning tasks, it is a generative graphical model, or alternatively a type of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. More precisely, each layer acts as a feature detector on inputs and serves as the visible layer for the next. In this way it is possible to have a fast unsupervised training procedure which will be very useful for the purpose of our project due to the complexity of the domain. 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 network studying their potentiality and flexibility; we'll try different configuration of network in order to obtain the good performances we're looking for.
