Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
en:projects:details:neuralsemproject [2011/09/23 15:13]
amir
en:projects:details:neuralsemproject [2013/06/12 13:52]
amir
Line 11: Line 11:
 */ */
 ---- dataentry project ---- ---- dataentry project ----
-title : Simple neural networks with error correcting abilities and high storage capacities ​+title : Implementing Some Feature Extracting Techniques to Model Human Visual System
 contactname:​ Amir Hesam Salavati contactname:​ Amir Hesam Salavati
 contactmail_mail:​ hesam.salavati@epfl.ch contactmail_mail:​ hesam.salavati@epfl.ch
Line 17: Line 17:
 contactroom:​ BC 160 contactroom:​ BC 160
 type : master semester type : master semester
-status : available+status : completed
 created_dt : 2010-11-15 created_dt : 2010-11-15
-taken_dt : YYYY-MM-DD +taken_dt : 2013-01-19 
-completed_dt : YYYY-MM-DD +completed_dt : 2013-06-12 
-by : the full name of the student +by : Diego Marcos Gonzalez ​ 
-output_media : en:​projects:​neural_storage_capacity.pdf|Download ​Abstract ​in PDF Format+output_media : en:​projects:​master_semester:​marcos_salavati_semester_project_report_2013.pdf|Download ​Project Report ​in PDF Format
 table : projects table : projects
 ====== ======
 template:​datatemplates:​project template:​datatemplates:​project
 ---- ----
 +\\ 
 +\\
 /* Description of the project */ /* Description of the project */
-===== Background ===== 
-Memorizing patterns and correctly recalling them later is an essential ingredient of neural activity. In past 25 years, a number of neural networks ​ has been invented memorize and recall patterns. Interestingly,​ some of these networks are able to recall the correct pattern even if the input pattern contains error and is partially corrupted because of noise. In this regard, these artificial networks resemble error correcting codes, i.e. they are able to recognize the correct pattern in presence of noise. 
- 
-However, the storage capacity of these networks are quite small compared to their counterparts in coding theory. Given the fact that modern codes use the same basic structure to do error correction and the one used by neural networks, i.e. a bipartite graph with local message passing, it seems interesting to consider applications of modern coding theory to increase the storage capacity of neural networks by finding the appropriate weights as well as proper update rules for the neural graph. 
- 
- 
 ===== Project Description ===== ===== Project Description =====
-This project has three phases: +\\ 
-  ​- ​In the first phase, we become familiar with the main concepts of neural networks, associative memory and, to some extent, codes on graphs as well as compressed sensing +In computer visionthere are various different techniques ​to extract important features from imagesThese features are then later used in patten recognition,​ image classificationetcSome of these techniques are comparable to some models ​of specific parts in human visual system
-  - Nextwe focus on implementing proper neural update rules for a specific neural network with error correcting capability and compare them based on their convergence speed and error correction abilityThis phase is more  +
-  - Finally, if time admits, we concentrate on the process ​of learning the connectivity matrix ​of such neural networks from training data sets+
  
-The first phase requires reading ​some background literature. The second one needs programming skills ​(preferably in MATLABin order to implement the considered algorithms. Finally, the third step is combination ​of theoretical work (doing research) and implementing ​some new ideas to find the best one in practice+In this project, we are interested in implementing ​some of the widely used techniques in feature learning ​(extractionand applying them to a dataset ​of natural images. This usually corresponds to solving ​some optimization problem ​to find the features that represent the data more accurately.
  
-This project is suitable for students interested in neural networks, coding theory ​and mathematics who prefer a combination of theoretical and empirical works. ​+The implementation can be either done in C or MATLAB (MATLAB is preferred).  
 + 
 +And here are some lines to give you an idea about why we are interested in this project:  
 +Once the feature extraction techniques are implemented,​ the learned features will then be used as inputs to a neural network which mimics some parts of human memory (neural associative memory). The ultimate goal would be to see if one will get better information storage capacities in artificial neural memories when the inputs are natural stimuli (such as images) and pre-processed before being stored. HEre, pre-processing refers to the feature extraction procedure. 
 +\\ 
 +\\ 
 +This project is suitable for students interested in computer vision, ​neural networks and mathematics who prefer a combination of theoretical and empirical works. ​
  
 The prerequisites are: The prerequisites are:
-1)Basic knowledge of coding theory and linear algebra ​(for the last phase)+1)Basic knowledge of linear algebra. ​
 2)Being familiar with a suitable programming language (MATLAB is preferred. But C/C++ is acceptable.) 2)Being familiar with a suitable programming language (MATLAB is preferred. But C/C++ is acceptable.)
 +
 +Some knowledge about feature learning models in computer vision is not necessary but is deeply encouraged. ​
 +
 +\\
 +\\
 +\\
 +
 +====== Report ======
 +The report ​ is available via the following link:
 +\\
 +{{:​en:​projects:​master_semester:​marcos_salavati_semester_project_report_2013.pdf|Implementing Some Feature Extracting Techniques to Model Human Visual System}}