Make the CNN to say "I don't know" I am currently working on an image classification problem. To ease the implementation I used transfer learning in Keras with Resnet50 as base model.
I have 2 classes, however there are many possibilities that the input image may not belong to both the classes. In such cases the CNN should be able to output as Unknown. 
Note : I can not create a separate class for unknown as it can come from any distribution.

I read that the Bayesian neural network would help in such cases.

Could you please help me in understanding how can I implement Bayesian CNN with transfer learning. Any material or reference link would be really helpful.

Thank you
You could put a one-class classification model before your CNN.

This would mean that you treat both your classes as one and then frame it as an anomaly detection problem. There are some different ways of achieving this. One way could be to do dimensionality reduction on the images and then use that image encoding to train an outlier detector like an one-class SVM. You can get some ideas here:

Learning Deep Features for One-ClassClassification

Anomaly Detection using One-Class Neural Networks

You can also look at cbeleites answer to this related questions. This was actually where I learned about one-class classification on images and it goes into a bit more detail and explains some caveats with using it.