Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI
  Reconstruction   Recent sparse MRI reconstruction models have used Deep Neural Networks (DNNs)
to reconstruct relatively high-quality images from highly undersampled k-space
data, enabling much faster MRI scanning. However, these techniques sometimes
struggle to reconstruct sharp images that preserve fine detail while
maintaining a natural appearance. In this work, we enhance the image quality by
using a Conditional Wasserstein Generative Adversarial Network combined with a
novel Adaptive Gradient Balancing technique that stabilizes the training and
minimizes the degree of artifacts, while maintaining a high-quality
reconstruction that produces sharper images than other techniques.

23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
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The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th