Learning from mistakes - learning optimally sparse image filters by quotient minimisation
49 mins 4 secs,
714.76 MB,
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| Description: |
Schönlieb, C
Wednesday 17th January 2018 - 11:45 to 12:30 |
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| Created: | 2018-01-17 15:12 |
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| Collection: | Statistical scalability |
| Publisher: | Isaac Newton Institute |
| Copyright: | Schönlieb, C |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Co-authors: Martin Benning (University of Cambridge), Guy Gilboa (Technion, Haifa), Joana Grah (University of Cambridge)
Learning approaches have recently become very popular in the field of inverse problems. A large variety of methods has been established in recent years, ranging from bi-level learning to high-dimensional machine learning techniques. Most learning approaches, however, only aim at fitting parametrised models to favourable training data whilst ig- noring misfit training data completely. In this talk, we fol- low up on the idea of learning parametrised regularisation functions by quotient minimisation. We consider one- and higher-dimensional filter functions to be learned and allow for fit- and misfit-training data consisting of multiple func- tions. We first present results resembling behaviour of well- established derivative-based sparse regularisers like total variation or higher-order total variation in one-dimension. Then, we introduce novel families of non-derivative-based regularisers. This is accomplished by learning favourable scales and geometric properties while at the same time avoiding unfavourable ones. |
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