<p>if I get your question right, it is about a noisy training data set? 
If so, there are some paper available which address this problem - e.g. </p>

<p><a href="https://arxiv.org/pdf/1705.03419.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1705.03419.pdf</a></p>

<p>or </p>

<p><a href="https://jingdongwang2017.github.io/Pubs/CVPR16-DisturbLabel.pdf" rel="nofollow noreferrer">https://jingdongwang2017.github.io/Pubs/CVPR16-DisturbLabel.pdf</a></p>

<p>Hope those papers are a little helpful :)</p>
it is possible to consider the uncertainties of labels when training CNNs for image object recognition
<p>I have automatically created a data set for object detection for camera images. However, the algorithm I use for this makes mistakes. But I can calculate the uncertainty</p>

<p><em>Now my question: Is it possible to consider these uncertainties when training a neural net? If so, how? Has one of you ever read a paper about it?</em> </p>

<p>Unfortunately I couldn't find anything about it myself. (Maybe I just use the wrong keywords in my search)</p>

<p>First of all, thank you very much for your help! </p>

<p>ps: A few details: I got a robot with a lidar who is calculating his posision of an object with a common slam-algorithm. I can calculate the covriance matrix for the position. I use these information to create the label for the 2d image. Later on I'd like to use a cheap camera to do the same job as the lidar</p>