Developing new methods to solve complex problems from limited training data.
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.
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.
To succeed in our innovation areas there is an absolute need to research new methodology to learn from limited and complex training data.
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.
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.
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.
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.