Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-supervised machine learning model for analysis of nanowire
  morphologies from transmission electron microscopy images   In the field of soft materials, microscopy is the first and often only
accessible method for structural characterization. There is a growing interest
in the development of machine learning methods that can automate the analysis
and interpretation of microscopy images. Typically training of machine learning
models require large numbers of images with associated structural labels,
however, manual labeling of images requires domain knowledge and is prone to
human error and subjectivity. To overcome these limitations, we present a
self-supervised transfer learning approach that uses a small number of labeled
microscopy images for training and performs as effectively as methods trained
on significantly larger data sets. Specifically, we train an image encoder with
unlabeled images and use that encoder for transfer learning of different
downstream image tasks (classification and segmentation) with a minimal number
of labeled images for training.

16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas