Learning from limited training data

Background

Deep learning methods have the strength of steadily improving performance with more training data. In the real world, the availability of suitable training data will often be limited, and annotation of complex image data requires domain experts and is both costly and time consuming. To succeed in our innovation areas there is an absolute need to research new methodology to learn from limited and complex training data.

Challenges

For real-life applications with complex images, training data will often be limited in the sense that annotations (labels) will often be sparse, even if the amount of acquired data may be vast. Annotations may also be incomplete or inconsistent (noisy) and they are generally made for other purposes than training machine learning algorithms, and thus may be less suited for that purpose. Moreover, the characteristics of complex image data are often very different from the standard images, making the current transfer learning go-to solution, based on pre-trained ImageNet models, infeasible because the image data of interest is statistically out-of-distribution with respect to the base model.

Main objective

To develop new deep learning methods to solve complex problems from limited training data.

Related news

New paper published in Machine Learning (2021)
June 24, 2021

Ahcène Boubekki, Michael Kampffmeyer, Ulf Brefeld and Robert Jenssen have published their paper Joint optimization of an autoencoder for clustering and embedding in journal Machine Learning (2021).

Stream our latest seminars
May 27, 2021

Did you miss any of our recent seminars? When we host seminars and events we often record relevant talks and presentations and make them available at our youtube channel. You can access all our content through our "outreach" page.

Annual report 2020
May 20, 2021

SFI Visual Intelligence has published the annual report for 2020. The report is approved by the Visual Intelligence board and available for download as a pdf document under "publicity".

Visual Intelligence Graduate School (VIGS)
May 20, 2021

SFI Visual Intelligence is organizing a graduate school for early career research fellows connected to Visual Intelligence. VIGS aims at connecting research fellows across our different research institutions to build social and professional networks.

Paper accepted at CVPR 2021
March 19, 2021

We are proud to announce that the paper “Reconsidering Representation Alignment for Multi-view Clustering” by Daniel J. Trosten, Sigurd Løkse, Robert Jenssen and Michael Kampffmeyer was recently accepted at the Conference on Computer Vision and Pattern Recognition 2021!

Official opening of Visual Intelligence research centre!
January 19, 2021

January 14, 2021 the official opening of SFI Visual Intelligence will be organized at the UiT - The Arctic University of Norway. Anne Husebekk, the rector of UiT will be giving a speech at the opening ceremony.

Northern Lights Deep Learning Workshop 2021
January 19, 2021

NLDL 2021 will be a digital conference hosted by the UiT Machine Learning Group and Visual Intelligence January 18-20. The program includes a Mini Deep Learning School the 18th and is followed by a tight program the rest of the week.

A new Centre for Research-based Innovation
January 19, 2021

Visual Intelligence will be one of the new SFIs funded by the Research Council of Norway. The center will run over a period of eight years and will form a collaboration between businesses and research institutions in Norway.

Visual Intelligence is officially opened!
January 19, 2021

The official opening of SFI Visual Intelligence was successfully arranged as a digital event today. We are now ready to commence our research and innovation to tackle some of the large challenges in deep learning and AI, along with our partners.

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