  We introduce algorithms to visualize feature spaces used by object detectors.
Our method works by inverting a visual feature back to multiple natural images.
We found that these visualizations allow us to analyze object detection systems
in new ways and gain new insight into the detector's failures. For example,
when we visualize the features for high scoring false alarms, we discovered
that, although they are clearly wrong in image space, they do look deceptively
similar to true positives in feature space. This result suggests that many of
these false alarms are caused by our choice of feature space, and supports that
creating a better learning algorithm or building bigger datasets is unlikely to
correct these errors. By visualizing feature spaces, we can gain a more
intuitive understanding of recognition systems.
