Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Application of machine learning methods to detect and classify Core
  images using GAN and texture recognition   During exploration campaigns, oil companies rely heavily on drill core
samples as they provide valuable geological information that helps them find
important oil deposits. Traditional core logging techniques are laborious and
subjective. Core imaging, a new technique in the oil industry, is used to
supplement analysis by rapidly characterising large quantities of drill cores
in a nondestructive and noninvasive manner. In this paper, we will present the
problem of core detection and classification. The first problem is detecting
the cores and segmenting the holes in images by using Faster RCNN and Mask RCNN
models respectively. The second problem is filling the hole in the core image
by applying the Generative adversarial network(GAN) technique and using
Contextual Residual Aggregation(CRA) which creates high frequency residual for
missing contents in images. And finally applying Texture recognition models for
the classification of core images.

23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph