  Many real-world classification problems often have classes with very few
labeled training samples. Moreover, all possible classes may not be initially
available for training, and may be given incrementally. Deep learning models
need to deal with this two-fold problem in order to perform well in real-life
situations. In this paper, we propose a novel Few-Shot Lifelong Learning (FSLL)
method that enables deep learning models to perform lifelong/continual learning
on few-shot data. Our method selects very few parameters from the model for
training every new set of classes instead of training the full model. This
helps in preventing overfitting. We choose the few parameters from the model in
such a way that only the currently unimportant parameters get selected. By
keeping the important parameters in the model intact, our approach minimizes
catastrophic forgetting. Furthermore, we minimize the cosine similarity between
the new and the old class prototypes in order to maximize their separation,
thereby improving the classification performance. We also show that integrating
our method with self-supervision improves the model performance significantly.
We experimentally show that our method significantly outperforms existing
methods on the miniImageNet, CIFAR-100, and CUB-200 datasets. Specifically, we
outperform the state-of-the-art method by an absolute margin of 19.27% for the
CUB dataset.
