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Could you give a link to a paper? I could take a unet and plug a classifier to it hmm. I tried something like that before but it did not work that well. Maybe

• acollins1331 7y ago
  https://jacobgil.github.io/deeplearning/class-activation-maps https://jacobgil.github.io/deeplearning/class-activation-map... Not sure how that would work with a segmentation algorithm like Unet though. It makes more sense in showing where in an image that the network was activated to give the image a label. AFAIK Unet gives ever pixel a label so I don't see how you could do what the parent describes.
  • tlear 7y ago
    That is activation mapping. I understood that what he was suggesting is using another objective that is optimized to define why network decided what it did. But need loss function for that For let’s say identifying a car we could come up with a labeled dataset that not only has classes but also different parts of the car labeled that we think differentiate car from a motorcycle or a whatever. Then model has to output both class and also segmentation or bounding boxes of the parts Then we combine both losses from these outputs and train. I tried using unet by attaching layers to it in different ways to interpret the segmentation. I could not get it to work well though