  In few-shot learning scenarios, the challenge is to generalize and perform
well on new unseen examples when only very few labeled examples are available
for each task. Model-agnostic meta-learning (MAML) has gained the popularity as
one of the representative few-shot learning methods for its flexibility and
applicability to diverse problems. However, MAML and its variants often resort
to a simple loss function without any auxiliary loss function or regularization
terms that can help achieve better generalization. The problem lies in that
each application and task may require different auxiliary loss function,
especially when tasks are diverse and distinct. Instead of attempting to
hand-design an auxiliary loss function for each application and task, we
introduce a new meta-learning framework with a loss function that adapts to
each task. Our proposed framework, named Meta-Learning with Task-Adaptive Loss
Function (MeTAL), demonstrates the effectiveness and the flexibility across
various domains, such as few-shot classification and few-shot regression.
