I have a multi-class classification problem where the algorithm should detect (and later on classify) new classes.
An example for such a task could be classifying if an image shows a dog or a cat. Furthermore, the model should be able to recognize that a goose doesn't fit into one of these two categories, thus create a new class.
How can the model detect new classes?
- Some unsupervised clustering algorithm
- When all classes are predicted by a value beyond a certain threshold?
Is there a (proven) model, which can handle a growing number of classes to classify - without expensive retraining of all the other classes?
- one vs all?
- something completely different?
I greatly appreciate every form of help and experiences you had with such a problem. References to papers or tutorials would be great too. Thank you in advance.
Here are two links where similar questions were asked, but (at least for me) not fully answered.