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I work on detecting unusual objects in traffic images in order to improve the safety of self-driving cars letamp;#39;s say our car has a camera but how does the computer understand what it sees a popular method is to assign a label to every pixel for example the road pixels are black the car pixels are blue and three pixels are green this is called semantic segmentation and it is often done by deep neural networks they work very well but require many training examples thousands of images with known labels we have those labels for the common classes like Road a car buildings the plants persons and so on however when we show unusual objects such as animals lost cargo rocks and so on the network has never seen them before and it produces nonsense outputs or fails to detect the object I work on a method to detect those cases and issue a warning that an unknown object is present my approach involves reconstructing the image from the semantic labels then I find the differences with the orig
