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In this era of digital information explosion, an abundance of data fromnumerous modalities is being generated as well as archived everyday. However,most problems associated with training Deep Neural Networks still revolvearound lack of data that is rich enough for a given task. Data is required notonly for training an initial model, but also for future learning tasks such asModel Compression and Incremental Learning. A diverse dataset may be used fortraining an initial model, but it may not be feasible to store it throughoutthe product life cycle due to data privacy issues or memory constraints. Wepropose to bridge the gap between the abundance of available data and lack ofrelevant data, for the future learning tasks of a given trained network. We usethe available data, that may be an imbalanced subset of the original trainingdataset, or a related domain dataset, to retrieve representative samples from atrained classifier, using a novel Data-enriching GAN (DeGAN) framework. Wedemonstrate that data from a related domain can be leveraged to achievestate-of-the-art performance for the tasks of Data-free Knowledge Distillationand Incremental Learning on benchmark datasets. We further demonstrate that ourproposed framework can enrich any data, even from unrelated domains, to make itmore useful for the future learning tasks of a given network.

DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier