I have build a conv net for image classification which work "well"
Now I extract features from last fully connected layer and use it for image retrieval (find image most similar to my target image) using hamming distance. it's working prety well even if I'm not able to predict how it'll be rotation invariant, sensible to noise..
I have try image retrieval only on class that have been seen by my model while training (but not training data). Do you know if it could work on class that have never been seen by the model while training?
Let's says model has been train on car and truck. I want to find most similar image to that yellow small car and it's work well it's return me yellow and small car.
Now let's imagine I apply it on a new data set with Plane. I want to find most similar image to that small yellow plane. Should I expect to find yellow small plane or that'll be totaly random?
Since network has never seen plane while training, is it possible to predict, or at least have an intuition, of the result?