Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic memory to alleviate catastrophic forgetting in continuous
  learning settings   In medical imaging, technical progress or changes in diagnostic procedures
lead to a continuous change in image appearance. Scanner manufacturer,
reconstruction kernel, dose, other protocol specific settings or administering
of contrast agents are examples that influence image content independent of the
scanned biology. Such domain and task shifts limit the applicability of machine
learning algorithms in the clinical routine by rendering models obsolete over
time. Here, we address the problem of data shifts in a continuous learning
scenario by adapting a model to unseen variations in the source domain while
counteracting catastrophic forgetting effects. Our method uses a dynamic memory
to facilitate rehearsal of a diverse training data subset to mitigate
forgetting. We evaluated our approach on routine clinical CT data obtained with
two different scanner protocols and synthetic classification tasks. Experiments
show that dynamic memory counters catastrophic forgetting in a setting with
multiple data shifts without the necessity for explicit knowledge about when
these shifts occur.

28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph