Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild   Lifelong learning with deep neural networks is well-known to suffer from
catastrophic forgetting: the performance on previous tasks drastically degrades
when learning a new task. To alleviate this effect, we propose to leverage a
large stream of unlabeled data easily obtainable in the wild. In particular, we
design a novel class-incremental learning scheme with (a) a new distillation
loss, termed global distillation, (b) a learning strategy to avoid overfitting
to the most recent task, and (c) a confidence-based sampling method to
effectively leverage unlabeled external data. Our experimental results on
various datasets, including CIFAR and ImageNet, demonstrate the superiority of
the proposed methods over prior methods, particularly when a stream of
unlabeled data is accessible: our method shows up to 15.8% higher accuracy and
46.5% less forgetting compared to the state-of-the-art method. The code is
available at https://github.com/kibok90/iccv2019-inc.

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The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci