Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Challenges in leveraging GANs for few-shot data augmentation   In this paper, we explore the use of GAN-based few-shot data augmentation as
a method to improve few-shot classification performance. We perform an
exploration into how a GAN can be fine-tuned for such a task (one of which is
in a class-incremental manner), as well as a rigorous empirical investigation
into how well these models can perform to improve few-shot classification. We
identify issues related to the difficulty of training such generative models
under a purely supervised regime with very few examples, as well as issues
regarding the evaluation protocols of existing works. We also find that in this
regime, classification accuracy is highly sensitive to how the classes of the
dataset are randomly split. Therefore, we propose a semi-supervised fine-tuning
approach as a more pragmatic way forward to address these problems.

17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol