| dc.date.accessioned |
2016-01-25T13:38:34Z |
und |
| dc.date.accessioned |
2017-10-24T12:24:08Z |
|
| dc.date.available |
2016-01-25T13:38:34Z |
und |
| dc.date.available |
2017-10-24T12:24:08Z |
|
| dc.date.issued |
2016-01-25T13:38:34Z |
|
| dc.identifier.uri |
http://radr.hulib.helsinki.fi/handle/10138.1/5252 |
und |
| dc.identifier.uri |
http://hdl.handle.net/10138.1/5252 |
|
| dc.title |
Unsupervised learning for image classification |
en |
| ethesis.discipline |
Computer science |
en |
| ethesis.discipline |
Tietojenkäsittelytiede |
fi |
| ethesis.discipline |
Datavetenskap |
sv |
| ethesis.discipline.URI |
http://data.hulib.helsinki.fi/id/1dcabbeb-f422-4eec-aaff-bb11d7501348 |
|
| ethesis.department.URI |
http://data.hulib.helsinki.fi/id/225405e8-3362-4197-a7fd-6e7b79e52d14 |
|
| ethesis.department |
Institutionen för datavetenskap |
sv |
| ethesis.department |
Department of Computer Science |
en |
| ethesis.department |
Tietojenkäsittelytieteen laitos |
fi |
| ethesis.faculty |
Matematisk-naturvetenskapliga fakulteten |
sv |
| ethesis.faculty |
Matemaattis-luonnontieteellinen tiedekunta |
fi |
| ethesis.faculty |
Faculty of Science |
en |
| ethesis.faculty.URI |
http://data.hulib.helsinki.fi/id/8d59209f-6614-4edd-9744-1ebdaf1d13ca |
|
| ethesis.university.URI |
http://data.hulib.helsinki.fi/id/50ae46d8-7ba9-4821-877c-c994c78b0d97 |
|
| ethesis.university |
Helsingfors universitet |
sv |
| ethesis.university |
University of Helsinki |
en |
| ethesis.university |
Helsingin yliopisto |
fi |
| dct.creator |
Lu, Yao |
|
| dct.issued |
2016 |
|
| dct.language.ISO639-2 |
eng |
|
| dct.abstract |
This thesis is an investigation of unsupervised learning for image classification. The state-of-the-art image classification method is Convolutional Neural Network (CNN), which is a purely supervised learning method. We argue that despite of the triumph of supervised learning, unsupervised learning is still important and compatible with supervised learning. For example, in the situation where some classes have no training data at all, so called zero-shot learning task, unsupervised learning can leverage supervised learning to classify the images of unseen classes. We proposed a new zero-shot learning method based on CNN and several unsupervised learning algorithms. Our method achieves the state-of-the-art results on the largest public available labelled image dataset, ImageNet fall2011. |
en |
| dct.language |
en |
|
| ethesis.language.URI |
http://data.hulib.helsinki.fi/id/languages/eng |
|
| ethesis.language |
English |
en |
| ethesis.language |
englanti |
fi |
| ethesis.language |
engelska |
sv |
| ethesis.thesistype |
pro gradu-avhandlingar |
sv |
| ethesis.thesistype |
pro gradu -tutkielmat |
fi |
| ethesis.thesistype |
master's thesis |
en |
| ethesis.thesistype.URI |
http://data.hulib.helsinki.fi/id/thesistypes/mastersthesis |
|
| dct.identifier.urn |
URN:NBN:fi-fe2017112252499 |
|
| dc.type.dcmitype |
Text |
|
