Usually this kind of finetuning is done by replacing the last or last few layers of a model and then training with additional data. However, it might be possible (if you want to keep the existing 91 classes) to just manually expand the net. If we switch the task to classification for a moment for an illustrative purposes, it might look something like:
(starting with 1024 input features, 1000 classes)

Allocating a new weight matrix with random initialization and shape 1024x1002 (+2 for “chicken” and “frog”)
Copying 1024x1000 weights from the pretrained model
Replace the linear layer of the original model, and set the weights to the new weight matrix.

I think this approach (for transfer learning) has been explored before (e.g., https://arxiv.org/pdf/1511.05641.pdf 1). It seems it might be important to initialize the weights for the additional classes properly, so you might play around with things like setting the mean of the weights for the additional classes to match the mean of the others, etc…
I’d be interested to see if this works, so it would be great if you could update this thread with experimental results.