Meta-learning is good for learning new class with <10 samples.And it requires sort of pre-training with similar classes.

Is there good recent works to improve convergence for randomly-initialized networks using meta-learning? Last time I looked into https://ai.google/research/pubs/pub46116/ and rejected work at openreview,
So far results are worse than SGD and Adam. Or maybe ~0.1% faster convergence but consumes ~30% more computations.