Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Contrastive Learning on Imbalanced Seed Data via Open-World
  Sampling   Contrastive learning approaches have achieved great success in learning
visual representations with few labels of the target classes. That implies a
tantalizing possibility of scaling them up beyond a curated "seed" benchmark,
to incorporating more unlabeled images from the internet-scale external sources
to enhance its performance. However, in practice, larger amount of unlabeled
data will require more computing resources due to the bigger model size and
longer training needed. Moreover, open-world unlabeled data usually follows an
implicit long-tail class or attribute distribution, many of which also do not
belong to the target classes. Blindly leveraging all unlabeled data hence can
lead to the data imbalance as well as distraction issues. This motivates us to
seek a principled approach to strategically select unlabeled data from an
external source, in order to learn generalizable, balanced and diverse
representations for relevant classes. In this work, we present an open-world
unlabeled data sampling framework called Model-Aware K-center (MAK), which
follows three simple principles: (1) tailness, which encourages sampling of
examples from tail classes, by sorting the empirical contrastive loss
expectation (ECLE) of samples over random data augmentations; (2) proximity,
which rejects the out-of-distribution outliers that may distract training; and
(3) diversity, which ensures diversity in the set of sampled examples.
Empirically, using ImageNet-100-LT (without labels) as the seed dataset and two
"noisy" external data sources, we demonstrate that MAK can consistently improve
both the overall representation quality and the class balancedness of the
learned features, as evaluated via linear classifier evaluation on full-shot
and few-shot settings. The code is available at:
https://github.com/VITA-Group/MAK

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The categories of this paper are: q-alg
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The categories of this paper are: nucl-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: mtrl-th
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The categories of this paper are: funct-an
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The categories of this paper are: stat
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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
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The categories of this paper are: ao-sci
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ex
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The categories of this paper are: math