Transfer Learning

In which of the following cases can we use transfer learning? More than one options may be correct.

We have a massive dataset in one domain and a smaller dataset in other

similar domain.

We have a pre$-$trained model in one domain and a massive dataset in oth

er similar domain

Ne have a pre$-$trained model in one domain and a smaller dataset in anot

her similar domainSuppose you make two CNN architectures, one with $15$ layers $($A$)$ and another with $35$ layers $($B$),$ and train them both $($with identical infrastructure, training scheme etc.$)$ on a $10-$class classification task. The performance of the models is as follows: Model$-$A: Training accuracy $=85\%,$ validation accuracy $=82\%$ Model$-$B: Training accuracy $=78\%,$ validation accuracy $=73\%$ Which of the following could be a possible explanation for these results?