  In the recent times, autoencoders, besides being used for compression, have
been proven quite useful even for regenerating similar images or help in image
denoising. They have also been explored for anomaly detection in a few cases.
However, due to location invariance property of convolutional neural network,
autoencoders tend to learn from or search for learned features in the complete
image. This creates issues when all the items in the image are not equally
important and their location matters. For such cases, a semi supervised
solution - regional priority based autoencoder (RPAE) has been proposed. In
this model, similar to object detection models, a region proposal network
identifies the relevant areas in the images as belonging to one of the
predefined categories and then those bounding boxes are fed into appropriate
decoder based on the category they belong to. Finally, the error scores from
all the decoders are combined based on their importance to provide total
reconstruction error.
