  Pretrained deep models hold their learnt knowledge in the form of model
parameters. These parameters act as "memory" for the trained models and help
them generalize well on unseen data. However, in absence of training data, the
utility of a trained model is merely limited to either inference or better
initialization towards a target task. In this paper, we go further and extract
synthetic data by leveraging the learnt model parameters. We dub them "Data
Impressions", which act as proxy to the training data and can be used to
realize a variety of tasks. These are useful in scenarios where only the
pretrained models are available and the training data is not shared (e.g., due
to privacy or sensitivity concerns). We show the applicability of data
impressions in solving several computer vision tasks such as unsupervised
domain adaptation, continual learning as well as knowledge distillation. We
also study the adversarial robustness of lightweight models trained via
knowledge distillation using these data impressions. Further, we demonstrate
the efficacy of data impressions in generating data-free Universal Adversarial
Perturbations (UAPs) with better fooling rates. Extensive experiments performed
on benchmark datasets demonstrate competitive performance achieved using data
impressions in absence of original training data.
