  Knowledge transfer among multiple networks using their outputs or
intermediate activations have evolved through extensive manual design from a
simple teacher-student approach (knowledge distillation) to a bidirectional
cohort one (deep mutual learning). The key factors of such knowledge transfer
involve the network size, the number of networks, the transfer direction, and
the design of the loss function. However, because these factors are enormous
when combined and become intricately entangled, the methods of conventional
knowledge transfer have explored only limited combinations. In this paper, we
propose a new graph-based approach for more flexible and diverse combinations
of knowledge transfer. To achieve the knowledge transfer, we propose a novel
graph representation called knowledge transfer graph that provides a unified
view of the knowledge transfer and has the potential to represent diverse
knowledge transfer patterns. We also propose four gate functions that are
introduced into loss functions. The four gates, which control the gradient, can
deliver diverse combinations of knowledge transfer. Searching the graph
structure enables us to discover more effective knowledge transfer methods than
a manually designed one. Experimental results on the CIFAR-10, -100, and
Tiny-ImageNet datasets show that the proposed method achieved significant
performance improvements and was able to find remarkable graph structures.
