I have a neural network which has been trained over some dataset. Say the dataset had 10k data points initially and another 100 data points are now added. Is there a way for my neural network to learn this entire (updated) dataset without training from scratch? Further, is catastrophic interference applicable here? I know catastrophic interference is applicable when the NN tries to learn "new information", but I wasn't sure if "updated (due to insertions) information" counts as "new information".
Indeed, unfortunately catastrophic interference (or forgetting) is applicable to your case. But there is a branch of Deep Learning that focuses on that problem called Continual Learning.