  A representation is supposed universal if it encodes any element of the
visual world (e.g., objects, scenes) in any configuration (e.g., scale,
context). While not expecting pure universal representations, the goal in the
literature is to improve the universality level, starting from a representation
with a certain level. To do so, the state-of-the-art consists in learning
CNN-based representations on a diversified training problem (e.g., ImageNet
modified by adding annotated data). While it effectively increases
universality, such approach still requires a large amount of efforts to satisfy
the needs in annotated data. In this work, we propose two methods to improve
universality, but pay special attention to limit the need of annotated data. We
also propose a unified framework of the methods based on the diversifying of
the training problem. Finally, to better match Atkinson's cognitive study about
universal human representations, we proposed to rely on the transfer-learning
scheme as well as a new metric to evaluate universality. This latter, aims us
to demonstrates the interest of our methods on 10 target-problems, relating to
the classification task and a variety of visual domains.
