Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Imaginative Generative Adversarial Network: Automatic Data
  Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action
  Recognition   Deep learning approaches deliver state-of-the-art performance in recognition
of spatiotemporal human motion data. However, one of the main challenges in
these recognition tasks is limited available training data. Insufficient
training data results in over-fitting and data augmentation is one approach to
address this challenge. Existing data augmentation strategies, such as
transformations including scaling, shifting and interpolating, require
hyperparameter optimization that can easily cost hundreds of GPU hours. In this
paper, we present a novel automatic data augmentation model, the Imaginative
Generative Adversarial Network (GAN) that approximates the distribution of the
input data and samples new data from this distribution. It is automatic in that
it requires no data inspection and little hyperparameter tuning and therefore
it is a low-cost and low-effort approach to generate synthetic data. The
proposed data augmentation strategy is fast to train and the synthetic data
leads to higher recognition accuracy than using data augmented with a classical
approach.

16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org