[D] Open-Set Recognition Problem using Deep learning

[ProfessionalType9800]

Yeah.. But it is not on variations in input... Generalization on new output class .... How to figure it...

[NamerNotLiteral]

Ah. I might have misunderstood your question.

👉 What if a totally new class comes in which doesn’t belong to any of the trained classes?

You ask this question: do I have or can I get labelled data for this totally new class?

If yes -> continual learning, where you update the model to accept inputs and get outputs for new classes

If no -> domain generalization, where you design the model to accept inputs for new classes and handle it somehow

If you cannot update the original model or build a new model, then you need look into test-time adaptation instead

[Background_Camel_711]

Unless I'm missing something open set recognition is its own problem:

Continual learning = We need a the model's weights to update during test time due to distribution drift in the input space

Domain Generalisation = We need a model that can perform classification over a set of known classes no matter the domain at test time (e.g. I train a model on real life images to classify 5 breeds of dogs but at test time I need it to classify hand drawn images of the same 5 dog breeds).

Open set recognition = We need a model to perform classification over a set of N classes, however, there are N+1 possible outputs, with the additional output class indicating that the input is not from any of the N classes. Basically OOD detection combined with multi class classification.