Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Active Class Incremental Learning for Imbalanced Datasets   Incremental Learning (IL) allows AI systems to adapt to streamed data. Most
existing algorithms make two strong hypotheses which reduce the realism of the
incremental scenario: (1) new data are assumed to be readily annotated when
streamed and (2) tests are run with balanced datasets while most real-life
datasets are actually imbalanced. These hypotheses are discarded and the
resulting challenges are tackled with a combination of active and imbalanced
learning. We introduce sample acquisition functions which tackle imbalance and
are compatible with IL constraints. We also consider IL as an imbalanced
learning problem instead of the established usage of knowledge distillation
against catastrophic forgetting. Here, imbalance effects are reduced during
inference through class prediction scaling. Evaluation is done with four visual
datasets and compares existing and proposed sample acquisition functions.
Results indicate that the proposed contributions have a positive effect and
reduce the gap between active and standard IL performance.

12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
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The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys