Given a neural network for image classification with $1000$ classes, the objective is to create another model which will output the probability of the neural network giving the correct prediction for a specific input image.
My ideas so far have been:
- Creating a convolutional network and training it with raw images together with a label which indicates if the first NN predicted it correct or not.
- Creating a fully connected network and training it with outputs of hidden layers/features of the first NN instead of the raw image, together with the labels as before.
- Creating a fully connected network and training it with the top-k outputs of the softmax layer of the first NN together with the labels.
The first method yielded an accuracy of $0.51$, the second $0.58$ and the last one $0.79$.
Can you suggest another method (or a modification of one from the above) which can achieve greater accuracy?