Autoencoder for MobileNetV2 I have way more unlabeled data than labeled data. Therefore I would like to train an Autoencoder using MobileNetV2 as the encoder. Then I will use the pretrained model for the classification of the labeled data.

I think it is rather difficult to "invert" the MobileNet architecture to create a Decoder. Therefore, my question is: can I use a different architecture for the decoder, or will this introduce weird artifacts?
can I use a different architecture for the decoder, or will this
  introduce weird artifacts?


If you are using U-net -like architecture with skip connection from corresponding encoding to decoding layer outputs of corresponding layers should have the same spatial resolution. There is no other commonly recognized limitations on decoder architecture for convolutional networks.