  Successful applications of deep learning (DL) requires large amount of
annotated data. This often restricts the benefits of employing DL to businesses
and individuals with large budgets for data-collection and computation.
Summarization offers a possible solution by creating much smaller
representative datasets that can allow real-time deep learning and analysis of
big data and thus democratize use of DL. In the proposed work, our aim is to
explore a novel approach to task-specific image corpus summarization using
semantic information and self-supervision. Our method uses a
classification-based Wasserstein generative adversarial network (CLSWGAN) as a
feature generating network. The model also leverages rotational invariance as
self-supervision and classification on another task. All these objectives are
added on a features from resnet34 to make it discriminative and robust. The
model then generates a summary at inference time by using K-means clustering in
the semantic embedding space. Thus, another main advantage of this model is
that it does not need to be retrained each time to obtain summaries of
different lengths which is an issue with current end-to-end models. We also
test our model efficacy by means of rigorous experiments both qualitatively and
quantitatively.
