Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automated Label Generation for Time Series Classification with
  Representation Learning: Reduction of Label Cost for Training   Time-series generated by end-users, edge devices, and different wearables are
mostly unlabelled. We propose a method to auto-generate labels of un-labelled
time-series, exploiting very few representative labelled time-series. Our
method is based on representation learning using Auto Encoded Compact Sequence
(AECS) with a choice of best distance measure. It performs self-correction in
iterations, by learning latent structure, as well as synthetically boosting
representative time-series using Variational-Auto-Encoder (VAE) to improve the
quality of labels. We have experimented with UCR and UCI archives, public
real-world univariate, multivariate time-series taken from different
application domains. Experimental results demonstrate that the proposed method
is very close to the performance achieved by fully supervised classification.
The proposed method not only produces close to benchmark results but
outperforms the benchmark performance in some cases.

22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex