  In real-world applications, data do not reflect the ones commonly used for
neural networks training, since they are usually few, unlabeled and can be
available as a stream. Hence many existing deep learning solutions suffer from
a limited range of applications, in particular in the case of online streaming
data that evolve over time. To narrow this gap, in this work we introduce a
novel and complex setting involving unsupervised meta-continual learning with
unbalanced tasks. These tasks are built through a clustering procedure applied
to a fitted embedding space. We exploit a meta-learning scheme that
simultaneously alleviates catastrophic forgetting and favors the generalization
to new tasks. Moreover, to encourage feature reuse during the
meta-optimization, we exploit a single inner loop taking advantage of an
aggregated representation achieved through the use of a self-attention
mechanism. Experimental results on few-shot learning benchmarks show
competitive performance even compared to the supervised case. Additionally, we
empirically observe that in an unsupervised scenario, the small tasks and the
variability in the clusters pooling play a crucial role in the generalization
capability of the network. Further, on complex datasets, the exploitation of
more clusters than the true number of classes leads to higher results, even
compared to the ones obtained with full supervision, suggesting that a
predefined partitioning into classes can miss relevant structural information.
