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

• URL: http://arxiv.org/abs/2010.09839v1
• Date: Mon, 19 Oct 2020 20:27:58 GMT
• Title: New Properties of the Data Distillation Method When Working With Tabular Data
• Authors: Dmitry Medvedev, Alexander D'yakonov
• Abstract summary: Data distillation is the problem of reducing the volume oftraining data while keeping only the necessary information. We show that the model trained on distilled samples can outperform the model trained on the original dataset.
• Score: 77.34726150561087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data distillation is the problem of reducing the volume oftraining data while keeping only the necessary information. With thispaper, we deeper explore the new data distillation algorithm, previouslydesigned for image data. Our experiments with tabular data show thatthe model trained on distilled samples can outperform the model trainedon the original dataset. One of the problems of the considered algorithmis that produced data has poor generalization on models with differenthyperparameters. We show that using multiple architectures during distillation can help overcome this problem.

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