Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Image quality assessment for machine learning tasks using
  meta-reinforcement learning   In this paper, we consider image quality assessment (IQA) as a measure of how
images are amenable with respect to a given downstream task, or task
amenability. When the task is performed using machine learning algorithms, such
as a neural-network-based task predictor for image classification or
segmentation, the performance of the task predictor provides an objective
estimate of task amenability. In this work, we use an IQA controller to predict
the task amenability which, itself being parameterised by neural networks, can
be trained simultaneously with the task predictor. We further develop a
meta-reinforcement learning framework to improve the adaptability for both IQA
controllers and task predictors, such that they can be fine-tuned efficiently
on new datasets or meta-tasks. We demonstrate the efficacy of the proposed
task-specific, adaptable IQA approach, using two clinical applications for
ultrasound-guided prostate intervention and pneumonia detection on X-ray
images.

9
The categories of this paper are: nlin
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas