Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MSD: Multi-Self-Distillation Learning via Multi-classifiers within Deep
  Neural Networks   As the development of neural networks, more and more deep neural networks are
adopted in various tasks, such as image classification. However, as the huge
computational overhead, these networks could not be applied on mobile devices
or other low latency scenes. To address this dilemma, multi-classifier
convolutional network is proposed to allow faster inference via early
classifiers with the corresponding classifiers. These networks utilize
sophisticated designing to increase the early classifier accuracy. However,
naively training the multi-classifier network could hurt the performance
(accuracy) of deep neural networks as early classifiers throughout interfere
with the feature generation process.
  In this paper, we propose a general training framework named
multi-self-distillation learning (MSD), which mining knowledge of different
classifiers within the same network and increase every classifier accuracy. Our
approach can be applied not only to multi-classifier networks, but also modern
CNNs (e.g., ResNet Series) augmented with additional side branch classifiers.
We use sampling-based branch augmentation technique to transform a
single-classifier network into a multi-classifier network. This reduces the gap
of capacity between different classifiers, and improves the effectiveness of
applying MSD. Our experiments show that MSD improves the accuracy of various
networks: enhancing the accuracy of every classifier significantly for existing
multi-classifier network (MSDNet), improving vanilla single-classifier networks
with internal classifiers with high accuracy, while also improving the final
accuracy.

11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat