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A Comparison of First-order Algorithms for Machine Learning
Yu Wei, Pock Thomas
Using an optimization algorithm to solve a machine learning problem is one of mainstreams in the field of science. In this work, we demonstrate a comprehensive comparison of some s…
iPiano: Inertial Proximal Algorithm for Non-Convex Optimization
Peter Ochs, Yunjin Chen, Thomas Brox +1
In this paper we study an algorithm for solving a minimization problem composed of a differentiable (possibly non-convex) and a convex (possibly non-differentiable) function. The a…
A higher-order MRF based variational model for multiplicative noise reduction
Yunjin Chen, Wensen Feng, René Ranftl +2
The Fields of Experts (FoE) image prior model, a filter-based higher-order Markov Random Fields (MRF) model, has been shown to be effective for many image restoration problems. Mot…
An inertial forward-backward algorithm for monotone inclusions
Dirk A. Lorenz, Thomas Pock
In this paper, we propose an inertial forward backward splitting algorithm to compute a zero of the sum of two monotone operators, with one of the two operators being co-coercive.…
Revisiting loss-specific training of filter-based MRFs for image restoration
Yunjin Chen, Thomas Pock, René Ranftl +1
It is now well known that Markov random fields (MRFs) are particularly effective for modeling image priors in low-level vision. Recent years have seen the emergence of two main app…
Learning -based analysis and synthesis sparsity priors using bi-level optimization
Yunjin Chen, Thomas Pock, Horst Bischof
We consider the analysis operator and synthesis dictionary learning problems based on the the regularized sparse representation model. We reveal the internal relations bet…
Insights into analysis operator learning: From patch-based sparse models to higher-order MRFs
Yunjin Chen, René Ranftl, Thomas Pock
This paper addresses a new learning algorithm for the recently introduced co-sparse analysis model. First, we give new insights into the co-sparse analysis model by establishing co…
A bi-level view of inpainting - based image compression
Yunjin Chen, René Ranftl, Thomas Pock
Inpainting based image compression approaches, especially linear and non-linear diffusion models, are an active research topic for lossy image compression. The major challenge in t…