Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Domain-Agnostic Clustering with Self-Distillation   Recent advancements in self-supervised learning have reduced the gap between
supervised and unsupervised representation learning. However, most
self-supervised and deep clustering techniques rely heavily on data
augmentation, rendering them ineffective for many learning tasks where
insufficient domain knowledge exists for performing augmentation. We propose a
new self-distillation based algorithm for domain-agnostic clustering. Our
method builds upon the existing deep clustering frameworks and requires no
separate student model. The proposed method outperforms existing domain
agnostic (augmentation-free) algorithms on CIFAR-10. We empirically demonstrate
that knowledge distillation can improve unsupervised representation learning by
extracting richer `dark knowledge' from the model than using predicted labels
alone. Preliminary experiments also suggest that self-distillation improves the
convergence of DeepCluster-v2.

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The categories of this paper are: nlin
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ph
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The categories of this paper are: supr-con
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The categories of this paper are: gr-qc
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The categories of this paper are: econ
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The categories of this paper are: solv-int
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The categories of this paper are: cmp-lg
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The categories of this paper are: bayes-an
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The categories of this paper are: astro-ph
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The categories of this paper are: ao-sci