Abstract
Recently, there have been increasing demands to construct compact deep
architectures to remove unnecessary redundancy and to improve the inference
speed. While many recent works focus on reducing the redundancy by eliminating
unneeded weight parameters, it is not possible to apply a single deep
architecture for multiple devices with different resources. When a new device
or circumstantial condition requires a new deep architecture, it is necessary
to construct and train a new network from scratch. In this work, we propose a
novel deep learning framework, called a nested sparse network, which exploits
an n-in-1-type nested structure in a neural network. A nested sparse network
consists of multiple levels of networks with a different sparsity ratio
associated with each level, and higher level networks share parameters with
lower level networks to enable stable nested learning. The proposed framework
realizes a resource-aware versatile architecture as the same network can meet
diverse resource requirements. Moreover, the proposed nested network can learn
different forms of knowledge in its internal networks at different levels,
enabling multiple tasks using a single network, such as coarse-to-fine
hierarchical classification. In order to train the proposed nested sparse
network, we propose efficient weight connection learning and channel and layer
scheduling strategies. We evaluate our network in multiple tasks, including
adaptive deep compression, knowledge distillation, and learning class
hierarchy, and demonstrate that nested sparse networks perform competitively,
but more efficiently, compared to existing methods.
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