Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Progressive Label Distillation: Learning Input-Efficient Deep Neural
  Networks   Much of the focus in the area of knowledge distillation has been on
distilling knowledge from a larger teacher network to a smaller student
network. However, there has been little research on how the concept of
distillation can be leveraged to distill the knowledge encapsulated in the
training data itself into a reduced form. In this study, we explore the concept
of progressive label distillation, where we leverage a series of
teacher-student network pairs to progressively generate distilled training data
for learning deep neural networks with greatly reduced input dimensions. To
investigate the efficacy of the proposed progressive label distillation
approach, we experimented with learning a deep limited vocabulary speech
recognition network based on generated 500ms input utterances distilled
progressively from 1000ms source training data, and demonstrated a significant
increase in test accuracy of almost 78% compared to direct learning.

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The categories of this paper are: hep-th
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: stat
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
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The categories of this paper are: nucl-ex
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The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int