Understanding model transferability [D]
I’m trying to reproduce and use the deep-clustering method introduced by Guo et. al (authors implementation ) on data from a physics experiment. Reproducing the authors results on MNIST was thankfully trivial as they provide an implementation. The transfer of application has so far, not worked at all, as we seem to be unable to attain a Normalized Mutual Information (nmi) score of over 0.3 on our data, and the adjusted rand scores match that level of poor performance.
My question is: why would I expect that the model cannot fit the data? Or how should I scale the model s.t. it retains the same properties as it had for the MNIST dataset (I have several thoughts on this but I don’t want to bias your thinking)?
Information about the data:
I’ve attached an image of each of the classes. They are visually very distinct, at least class 0 from the 1 and 2. I’ve not been able to
submitted by /u/titanandwire