Say we have data for N classes and train a classifier. Then we have a new set of data for a N+1th class. How do I train a classifier that now predicts all N+1 classes?
Setting is in object detection, though I don't think it should be different between image classification and object detection. Also, I am finetuning from a pre-trained model (e.g. VGG16 trained on image-net).
Option 1: Compile all the data for the N+1 classes and finetune again from the original VGG16 trained on image-net.
Option 2: Compile all the data for the N+1 classes and finetune from the model trained on N classes.
Option 3: Is there a way to finetune only using the data from the N+1th class? I assume no, since the last layer's weights (right before class prediction) need to be re-initialized.
Options 1 and 2 aren't ideal b/c it isn't scalable as we increase the number of classes.