Learning Loss for Active Learning in Depth Reconstruction Problem

Student: Gushchenko-cheverda Ivan

Educational Programme: Data Science (Master)

Year of Graduation: 2020

Accurate depth estimation from images is a fundamental task in deep learning. It has many applications including scene understanding and reconstruction. Datasets for supervised depth estimation are hard to obtain and usually does not contain sufficient number of images or sufficient variety of scenes. Since inputs for depth estimation are simple RGB images, it is easy to obtain large number of various unlabeled images. We consider that depth masks can be labeled by using manual marking. Thus, we researched the possibility of performing active learning approach for selecting unlabeled samples to be labeled. In this work we concentrated on using learning loss method to perform active learning train selection. We performed multiple experiments with learning loss algorithm and evaluated resulting model.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses