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Understanding model transferability [D]

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:

Number of images 46283 (can get more)
Number of classes 3
Class balance 2:1:2
Image dimensions 80x80x1 (or 128x128x1)

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

Example of class 0

example of class 1

Example of class 2

submitted by /u/titanandwire
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.