  Deep network compression has been achieved notable progress via knowledge
distillation, where a teacher-student learning manner is adopted by using
predetermined loss. Recently, more focuses have been transferred to employ the
adversarial training to minimize the discrepancy between distributions of
output from two networks. However, they always emphasize on result-oriented
learning while neglecting the scheme of process-oriented learning, leading to
the loss of rich information contained in the whole network pipeline. Inspired
by the assumption that, the small network can not perfectly mimic a large one
due to the huge gap of network scale, we propose a knowledge transfer method,
involving effective intermediate supervision, under the adversarial training
framework to learn the student network. To achieve powerful but highly compact
intermediate information representation, the squeezed knowledge is realized by
task-driven attention mechanism. Then, the transferred knowledge from teacher
network could accommodate the size of student network. As a result, the
proposed method integrates merits from both process-oriented and
result-oriented learning. Extensive experimental results on three typical
benchmark datasets, i.e., CIFAR-10, CIFAR-100, and ImageNet, demonstrate that
our method achieves highly superior performances against other state-of-the-art
methods.
