Fixed formatting

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 Explanation: detecting when an object detector fails by producing a prediction "this bounding box has object of class X", where in fact the object is of class Y which has not been encountered during training.
 
 **Baseline**: "Dropout Sampling for Robust Object Detection in Open-Set Conditions", Miller et al., ICRA 2018 
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 **Abstract**: They apply the SSD network as a starting point. The method they propose essentially consists of 1) enabling dropout during forward pass also and 2) feeding the same image through SSD multiple time (with dropout on, so result will be different each time!), and 3) filtering the resulting bounding boxes to detect the errors of the problem type.
 
 **Problem of baseline**: multiple forward passes can get rather wasteful in terms of required computation time (they need up to 20 per image)