Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Condensed Composite Memory Continual Learning   Deep Neural Networks (DNNs) suffer from a rapid decrease in performance when
trained on a sequence of tasks where only data of the most recent task is
available. This phenomenon, known as catastrophic forgetting, prevents DNNs
from accumulating knowledge over time. Overcoming catastrophic forgetting and
enabling continual learning is of great interest since it would enable the
application of DNNs in settings where unrestricted access to all the training
data at any time is not always possible, e.g. due to storage limitations or
legal issues. While many recently proposed methods for continual learning use
some training examples for rehearsal, their performance strongly depends on the
number of stored examples. In order to improve performance of rehearsal for
continual learning, especially for a small number of stored examples, we
propose a novel way of learning a small set of synthetic examples which capture
the essence of a complete dataset. Instead of directly learning these synthetic
examples, we learn a weighted combination of shared components for each example
that enables a significant increase in memory efficiency. We demonstrate the
performance of our method on commonly used datasets and compare it to recently
proposed related methods and baselines.

31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an