In https://arxiv.org/pdf/1809.09529.pdf it is said

> If the new dataset is **similar** to
original dataset, we expect higher-level features in the CNN
to be relevant to this dataset. Thus, it is recommended to train
a linear classifier on the CNN codes. Otherwise, if the new
dataset is large then we might try to fine-tune through the full
network. However if the new dataset is **different** from the
original dataset and it has a small size, then it might work
better to train a linear classifier from activations somewhere
earlier in the network.

Fine tuning is quite popular in image classification problems where you take a model pre-trained on ImageNet and fine tune it to your dataset. But ImageNet classifies images on a very broad spectrum (1000 classes), so what does **similar** and **different** even mean precisely? How do I know if my dataset is similar to such a big and broad dataset? What does that mean?