Firstly, great and interesting work on PAWS! I have been working on using PAWS for my specific uses and I had a couple questions. Have you ever used a pretrained model as the backbone and used PAWS for further training? Particularly what I'm trying to do is use a backbone trained on imagenet, and then use PAWS to tune the weights on a smaller dataset with very few labels per class. I was wondering if you've ever experimented with a training setup like this, and what you would recommend hyperparameter-wise.

My second question touches on the 'very few labels per class' I just mentioned. In the ablation study in the paper you go down to 4 labels per class. Have you experimented with anything lower? I've been working on training PAWS with one labeled image per class, and was wondering what insights you had on training in this case. I know you have mentioned that increasing the number of classes per support batch is important, but I was wondering if there was anything else.

Thanks so much!