Ground truth/label modification during training (with the data obtained from the

I'm working on an image segmentation algorithm with FCN (Long et al., 2015) as the backbone network.

One idea I have is to use the argmax binary mask obtained from the final score layer (250x250x1) to generate some data (e.g. number of blobs in the mask) to modify the ground truth (e.g. set some pixels in the gt mask to 'ignore' labels) or in some way (partly) extract from the features (similar to RPN layer in FasterRCNN).

Does this violate any deep learning or machine learning rules?

Topic labels deep-learning

Category Data Science

1
answered at

No - sounds like you are just stacking different neural networks. Neural networks are inherently stacked models. Sometimes those stacked models are homogeneous and sometimes those stack are heterogeneous.

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