I have 2 different models with each model doing a separate function and have been trained with different weights. Is there any way I can merge these two models to get a single model.

If it can be merged

  • How should I go about it? Will the number of layers remain the same?
  • Will it give me any performance gain?(Intuitively speaking, I should get a higher performance)
  • Will the hardware requirements change when using the new model?
  • Will I need to retrain the model? Can I somehow merge the trained weights?

If the models cannot be merged

  • Why so? After all, convolution is finding the correct pattern in data.
  • Also, if CNN's cannot be merged, then how do skip-connections like ResNet50 work?



What I currently have

Image ---(model A) ---> Temporary image ---(Model B)---> Output image

What I want:

Image ---(model C) ---> Output image


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