Name
Abstract | ||
|---|---|---|
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 ignoring misfit training data completely. In this paper, we follow up on the idea of learning parametrised regularisation functions by quotient minimisation as established in [3]. We extend the model therein to include higher-dimensional filter functions to be learned and allow for fit- and misfit-training data consisting of multiple functions. We first present results resembling behaviour of well-established derivative-based sparse regularisers like total variation or higher-order total variation in one-dimension. Our second and main contribution is the introduction of 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-58771-4_41 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Regularisation learning,Non-linear eigenproblem,Sparse regularisation,Generalised inverse power method | Training set,Mathematical optimization,Computer science,Quotient,Ranging,Minimisation (psychology),Artificial intelligence,Inverse problem,Machine learning | Conference |
Volume | ISSN | Citations |
10302 | 0302-9743 | 3 |
PageRank | References | Authors |
0.41 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Benning | 1 | 59 | 5.89 |
| Guy Gilboa | 2 | 1476 | 83.79 |
| Joana Grah | 3 | 7 | 1.24 |
| Carola-Bibiane Schönlieb | 4 | 334 | 39.39 |