About KNN in SCAN about unsupervised-classification

[wvangansbeke]

Hi @zhunzhong07,

Yes you're correct. We sample uniformly from the K nearest neighbors during training. Therefore, it is highly likely that the anchor sees a different neighbor in the next epoch. So, if you train long enough it should have the exact same effect as Eq. 2. After all, it is not practical to include a lot of neighbors for every sample during a forward pass, since this does not scale well with the amount K.

Hope this helps.

[zhunzhong07]

Hi @wvangansbeke,

Thanks for your quick reply. I have another question.

In your code, I find that the indices of neighbors only computed once after the self-supervised learning. Why not re-computed the neighbors after each epoch of SCAN. Will this improve the results?

[wvangansbeke]

Yes a good point. I never tried it exactly like that (although something similar). It makes sense actually. However, I'm not sure that the representations are going to be much better at that point. I just think that it will be difficult to exploit the selflabling as we currently do. This step basicaly readjusts the decision boundary between classes and updates the representations based on the prototypes of each class.

[zhunzhong07]

OK. Thanks for your reply!