> What I want to achieve is incremental training. So, as soon as I get new data, I can further train my already trained model and I don't have to retrain everything.

Learning without forgetting is one of the methods to solve multitask learning. If your model trained to solve problem A and then after sometimes you need your model to solve new problem B without forgetting the problem A (the model still good to solve the problem A), then you need this.

If your new data is the same task with the old data, you don't need to use multitask learning method. For example:

- if your model trained with 50 images to detect an apple in the image, and then you get new 100 images to detect an apple then you just need to continue your train (incremental learning).
- if your model trained with 100 images to detect an apple in the image, and then you get new 100 images to train your model to detect an orange and **you don't care if your model will give a bad result to detect an apple**, then you use transfer learning.
- if your model trained with 100 images to detect an apple in the image, and then you get new 100 images to detect an orange and your model must good to detect both apple and orange in an image, then you use multitask learning (e.g. learning without forgetting)