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 what I did sucked and there is a better way
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 what I did sucked and there is a better way
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.
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