Deep neural networks are powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong or whether the input is outside the range of which the system is expected to safely perform. For critical or automatic applications, knowledge about the confidence of predictions is essential.
For safety-critical applications, e.g. in health, a limitation of current deep learning systems is that they are in general not designed to recognize when their predictions may be wrong or to recognize with some certainty that the input is inside the range of which the system is expected to safely perform. The simple regularization technique "Dropout" provides a measure of variability, but not a statistically sound quantification of uncertainty propagating from input to output. Bayesian deep models are emerging but have so far been challenging to develop for complex image data due to the complexity of the input data and the nonlinear nature of the data processing.
To develop deep learning models that can estimate confidence and quantify uncertainty of their predictions.
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).
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.
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".
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.
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.