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dluther93 t1_it982ce wrote

I've done this before for multi-modal classification tasks.
Train CNN end-to-end, take the layer before last for a dense vector of embeddings.
use that dense vector as a feature set alongside my tabular data in an XGBoost or Catboost model. Boom

Easy to do on a local machine, cumbersome to try and reliably deploy this model though.

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Bonsanto t1_ita8okg wrote

Do you have any example/implementation at hand?

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dluther93 t1_itbglnd wrote

Nothing I’m able to pass off publicly unfortunately. Just build a cnn, then concat the outputs into your original dataset :)

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abstract000 t1_itc0l85 wrote

You really had a significant improvement? I tried this and it performed poorly, but maybe it was just the dataset. BTW did you test that on a Kaggle competition?

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dluther93 t1_itc1ldm wrote

It was significant to us. Our base case is the xgboost model with tabular data only. We were looking at ways to augment our tabular performance, not improve imaging performance. It was a method of feature engineering for the problem.

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abstract000 t1_itc3cw1 wrote

OK I will try this next time I work on tabular data.

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