  The goal of data-free meta-learning is to learn useful prior knowledge from a
collection of pre-trained models without accessing their training data.
However, existing works only solve the problem in parameter space, which (i)
ignore the fruitful data knowledge contained in the pre-trained models; (ii)
can not scale to large-scale pre-trained models; (iii) can only meta-learn
pre-trained models with the same network architecture. To address those issues,
we propose a unified framework, dubbed PURER, which contains: (1) ePisode
cUrriculum inveRsion (ECI) during data-free meta training; and (2) invErsion
calibRation following inner loop (ICFIL) during meta testing. During meta
training, we propose ECI to perform pseudo episode training for learning to
adapt fast to new unseen tasks. Specifically, we progressively synthesize a
sequence of pseudo episodes by distilling the training data from each
pre-trained model. The ECI adaptively increases the difficulty level of pseudo
episodes according to the real-time feedback of the meta model. We formulate
the optimization process of meta training with ECI as an adversarial form in an
end-to-end manner. During meta testing, we further propose a simple
plug-and-play supplement-ICFIL-only used during meta testing to narrow the gap
between meta training and meta testing task distribution. Extensive experiments
in various real-world scenarios show the superior performance of ours.
