  Model distillation is an effective and widely used technique to transfer
knowledge from a teacher to a student network. The typical application is to
transfer from a powerful large network or ensemble to a small network, that is
better suited to low-memory or fast execution requirements. In this paper, we
present a deep mutual learning (DML) strategy where, rather than one way
transfer between a static pre-defined teacher and a student, an ensemble of
students learn collaboratively and teach each other throughout the training
process. Our experiments show that a variety of network architectures benefit
from mutual learning and achieve compelling results on CIFAR-100 recognition
and Market-1501 person re-identification benchmarks. Surprisingly, it is
revealed that no prior powerful teacher network is necessary -- mutual learning
of a collection of simple student networks works, and moreover outperforms
distillation from a more powerful yet static teacher.
