Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Batch Curation for Unsupervised Contrastive Representation Learning   The state-of-the-art unsupervised contrastive visual representation learning
methods that have emerged recently (SimCLR, MoCo, SwAV) all make use of data
augmentations in order to construct a pretext task of instant discrimination
consisting of similar and dissimilar pairs of images. Similar pairs are
constructed by randomly extracting patches from the same image and applying
several other transformations such as color jittering or blurring, while
transformed patches from different image instances in a given batch are
regarded as dissimilar pairs. We argue that this approach can result similar
pairs that are \textit{semantically} dissimilar. In this work, we address this
problem by introducing a \textit{batch curation} scheme that selects batches
during the training process that are more inline with the underlying
contrastive objective. We provide insights into what constitutes beneficial
similar and dissimilar pairs as well as validate \textit{batch curation} on
CIFAR10 by integrating it in the SimCLR model.

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The categories of this paper are: nlin
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: math-ph
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The categories of this paper are: q-fin
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The categories of this paper are: physics
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The categories of this paper are: q-bio
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The categories of this paper are: hep-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an