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Title: Stochasticity and skip connections improve knowledge transfer

Paper content:
Deep neural networks have achieved state-of-the-art performance in various fields, but they have to be scaled down to be used for real-world applications.
As a means to reduce the size of a neural network while preserving its performance, knowledge transfer has brought a lot of attention.
One popular method of knowledge transfer is knowledge distillation (KD), where softened outputs of a pre-trained teacher network help train student networks.
Since KD, other transfer methods have been proposed, and they mainly focus on loss functions, activations of hidden layers, or additional modules to transfer knowledge well from teacher networks to student networks.
In this work, we focus on the structure of a teacher network to get the effect of multiple teacher networks without additional resources.
We propose changing the structure of a teacher network to have stochastic blocks and skip connections.
In doing so, a teacher network becomes the aggregate of a huge number of paths.
In the training phase, each sub-network is generated by dropping stochastic blocks randomly and used as a teacher network.
This allows training the student network with multiple teacher networks and further enhances the student network on the same resources in a single teacher network.
We verify that the proposed structure brings further improvement to student networks on benchmark datasets.
Deep neural networks (DNNs) have achieved state-of-theart performances on complex tasks like computer vision (He et al. 2016) , language modeling (Jozefowicz et al. 2016) , and machine translation .
Moreover, they surpass human ability in several fields including image classification (He et al. 2016) , the go game , voice generation (Oord et al. 2016) , and so on.
Despite their superior performance, it is difficult to use DNN-based models because of limited memory and computational resources in the embedded systems.
To deal with this problem, many studies have been done to make DNNs smaller but efficient to be applicable in resource limited cases.
One of them is knowledge transfer (KT), which train a smaller network with the information of large model's information.
Knowledge
The primary goal of this paper is to make a single teacher network to behave as multiple teacher networks.
Since multiple teacher networks provide various outputs on a given input, they can provide more extensive knowledge than a single teacher network does.
It has been shown that student networks improve further with multiple teacher networks which are used as an ensemble or separately (Hinton, Vinyals, and Dean 2015; You et al. 2017; Zhang et al. 2018) .
However, using multiple teacher networks is a resource burden and delays the training process.
In this work, we propose to add stochastic blocks and skip connections to a teacher network.
In doing so, we can get the effect of multiple teacher networks in the same resource of single teacher network.
A stochastic block is a block that falls with a fixed probability in the training phase and weighted by its survival probability in the inference phase .
Skip connections make huge number of paths in the network and function as memory which link the information of previous parts and later parts even if stochastic blocks drop.
In the training phase, different sub-networks are generated resulting from stochastic drop in the teacher network for each batch.
The sub-networks still have reliable performances since there still exist valid paths.
Each sub-network becomes a teacher network for each batch, so the student network is trained with multiple teacher networks in the entire training phase.
Figure 1 is example of sub-networks generated by dropping one block each from a network with the proposed structure.
The networks consists of 3 blocks and f i , Id represents the ith block of the network (i ∈ 1, 2, 3) and an identity block generated by a skip connection respectively.
Red arrows in the figure mean that the outputs of the blocks are 0.
In Figure 1 , even if one block drops, each subnetwork still has 4 valid paths of 8 total paths.
We observe that :
(i) multiple teacher networks are generated from a single teacher network with no more resources;
(ii) generated networks provide different knowledge to a student network;
(iii) the performances of student networks improve with the help of a teacher network of the proposed structure.
We succeeded in training the student network to perform better than the ones with the same architecture trained by the knowledge transfer methods (KD) (Hinton, Vinyals, and Dean 2015) , attention transfer (AT) (Zagoruyko and Komodakis 2016a) , and mutual learning (ML) (Zhang et al. 2018) ) over CIFAR-100 (Krizhevsky, Hinton, and others 2009 ) and tinyimageNet (Russakovsky et al. 2015) datasets.
The rest of this paper is organized as follows.
First, we review recent studies related to our work.
Then, we demonstrate the proposed scheme with details.
After this, we present experiments and discuss the results.
Finally, summary and concluding remarks are given in the conclusion.
In this work, we propose to change the structure of a teacher network to get the effect of multiple teacher networks in the same resource of one teacher network.
In our proposed structure, we obtain multiple teacher networks without additional resource so that compact networks improve further than those trained from conventional transfer methods.
The proposed structure can be easily applied to other transfer methods and tasks, e.g. segmentation or object detection.

assistant: text: The goal of this paper is to get the effect of multiple teacher networks by exploiting stochastic blocks and skip connections.