  Meta-learning models have two objectives. First, they need to be able to make
predictions over a range of task distributions while utilizing only a small
amount of training data. Second, they also need to adapt to new novel unseen
tasks at meta-test time again by using only a small amount of training data
from that task. It is the second objective where meta-learning models fail for
non-mutually exclusive tasks due to task overfitting. Given that guaranteeing
mutually exclusive tasks is often difficult, there is a significant need for
regularization methods that can help reduce the impact of task-memorization in
meta-learning. For example, in the case of N-way, K-shot classification
problems, tasks becomes non-mutually exclusive when the labels associated with
each task is fixed. Under this design, the model will simply memorize the class
labels of all the training tasks, and thus will fail to recognize a new task
(class) at meta-test time. A direct observable consequence of this memorization
is that the meta-learning model simply ignores the task-specific training data
in favor of directly classifying based on the test-data input. In our work, we
propose a regularization technique for meta-learning models that gives the
model designer more control over the information flow during meta-training. Our
method consists of a regularization function that is constructed by maximizing
the distance between task-summary statistics, in the case of black-box models
and task specific network parameters in the case of optimization based models
during meta-training. Our proposed regularization function shows an accuracy
boost of $\sim$ $36\%$ on the Omniglot dataset for 5-way, 1-shot classification
using black-box method and for 20-way, 1-shot classification problem using
optimization-based method.
