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Data distillation is the problem of reducing the volume oftraining data whilekeeping only the necessary information. With thispaper, we deeper explore thenew data distillation algorithm, previouslydesigned for image data. Ourexperiments with tabular data show thatthe model trained on distilled samplescan outperform the model trainedon the original dataset. One of the problems ofthe considered algorithmis that produced data has poor generalization on modelswith differenthyperparameters. We show that using multiple architectures duringdistillation can help overcome this problem.

New Properties of the Data Distillation Method When Working With Tabular Data