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.
Specific Questions:
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?
- one-class?
- 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.
Stackexchange: Streaming multi-class classification
Stackexchange multi-class classification word2vec
Stackoverflow multiclass classification growing number of classes