Autoencoder with Transfer Learning?
From what I know, there is no proven method to do this. I'd train the autoencoder from scratch. 
In theory, if you find a pre-trained CNN which does not use max pooling, you can use those weights and architecture for the encoder stage in your autoencoder. You can also extract features from a pre-trained model and concatenate/merge them to your autoencoder. But the value add is not clear, and the architecture might become overly complex.
Is there a way I can train an autoencoder model using a pre-trained model like ResNet?
I'm trying to train an autoencoder model with input as an image and output as a masked version of that image.
Is it possible to use weights from a pretrained model here?