  We present a novel Balanced Incremental Model Agnostic Meta Learning system
(BI-MAML) for learning multiple tasks. Our method implements a meta-update rule
to incrementally adapt its model to new tasks without forgetting old tasks.
Such a capability is not possible in current state-of-the-art MAML approaches.
These methods effectively adapt to new tasks, however, suffer from
'catastrophic forgetting' phenomena, in which new tasks that are streamed into
the model degrade the performance of the model on previously learned tasks. Our
system performs the meta-updates with only a few-shots and can successfully
accomplish them. Our key idea for achieving this is the design of balanced
learning strategy for the baseline model. The strategy sets the baseline model
to perform equally well on various tasks and incorporates time efficiency. The
balanced learning strategy enables BI-MAML to both outperform other
state-of-the-art models in terms of classification accuracy for existing tasks
and also accomplish efficient adaption to similar new tasks with less required
shots. We evaluate BI-MAML by conducting comparisons on two common benchmark
datasets with multiple number of image classification tasks. BI-MAML
performance demonstrates advantages in both accuracy and efficiency.
