Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Revisiting loss-specific training of filter-based MRFs for image
  restoration   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 approaches for learning the parameters in MRFs: (1)
probabilistic learning using sampling-based algorithms and (2) loss-specific
training based on MAP estimate. After investigating existing training
approaches, it turns out that the performance of the loss-specific training has
been significantly underestimated in existing work. In this paper, we revisit
this approach and use techniques from bi-level optimization to solve it. We
show that we can get a substantial gain in the final performance by solving the
lower-level problem in the bi-level framework with high accuracy using our
newly proposed algorithm. As a result, our trained model is on par with highly
specialized image denoising algorithms and clearly outperforms
probabilistically trained MRF models. Our findings suggest that for the
loss-specific training scheme, solving the lower-level problem with higher
accuracy is beneficial. Our trained model comes along with the additional
advantage, that inference is extremely efficient. Our GPU-based implementation
takes less than 1s to produce state-of-the-art performance.

5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph