Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning from a Teacher using Unlabeled Data   Knowledge distillation is a widely used technique for model compression. We
posit that the teacher model used in a distillation setup, captures
relationships between classes, that extend beyond the original dataset. We
empirically show that a teacher model can transfer this knowledge to a student
model even on an {\it out-of-distribution} dataset. Using this approach, we
show promising results on MNIST, CIFAR-10, and Caltech-256 datasets using
unlabeled image data from different sources. Our results are encouraging and
help shed further light from the perspective of understanding knowledge
distillation and utilizing unlabeled data to improve model quality.

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The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th