Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Parameter Tuning in Meta-learning for Computer Vision   Learning to learn plays a pivotal role in meta-learning (MTL) to obtain an
optimal learning model. In this paper, we investigate mage recognition for
unseen categories of a given dataset with limited training information. We
deploy a zero-shot learning (ZSL) algorithm to achieve this goal. We also
explore the effect of parameter tuning on performance of semantic auto-encoder
(SAE). We further address the parameter tuning problem for meta-learning,
especially focusing on zero-shot learning. By combining different embedded
parameters, we improved the accuracy of tuned-SAE. Advantages and disadvantages
of parameter tuning and its application in image classification are also
explored.

13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org