I want to use the encoder of my autoencoder for feature extraction in an image anomaly detection framework.
For that reason, I thought that pretraining the autoencoder on a large dataset and then fine-tuning it on my target dataset would be a good idea. This idea crossed my mind because many anomaly detection approaches use CNN architectures like VGG, ResNet etc. as a feature extractor, which are pretrained on ImageNet.
I did not find papers regarding this matter and therefore my question if transfer learning is even really used on autoencoders?