| DC Field | Value | Language |
|---|
| dc.contributor.author | ACHAB, HOussem | - |
| dc.date.accessioned | 2024-09-24T09:33:21Z | - |
| dc.date.available | 2024-09-24T09:33:21Z | - |
| dc.date.issued | 2024 | - |
| dc.identifier.uri | https://repository.esi-sba.dz/jspui/handle/123456789/660 | - |
| dc.description | Encadrant : Dr. ELARBI BOUDIHIR Mohamed Co-encadrant : Pr. RAHMOUN Abdellatif | en_US |
| dc.description.abstract | During the last few years, many breakthroughs in the field of semi-supervised learning
have proven to be very effective in overcoming the lack of labeled images caused by the
high cost of pixel-level labeling. Numerous approaches exploring the use of both labeled and
unlabeled images have been published. This thesis explores the current state of the art in
semi-supervised semantic segmentation, highlighting experimental results, current challenges,
and future research directions in this field. | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Semantic Image Segmentation | en_US |
| dc.subject | Deep Learning | en_US |
| dc.subject | Semi-Supervised Learning | en_US |
| dc.title | Étude comparative des méthodes semi-supervisées pour la segmentation sémantique | en_US |
| dc.type | Thesis | en_US |
| Appears in Collections: | Master
|
