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arXiv · 1803.09588

Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning Accuracy

Florian Scheidegger·Roxana Istrate·Giovanni Mariani·Luca Benini·Costas Bekas·Cristiano Malossi
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Abstract

In the deep-learning community new algorithms are published at an incredible pace. Therefore, solving an image classification problem for new datasets becomes a challenging task, as it requires to re-evaluate published algorithms and their different configurations in order to find a close to optimal classifier. To facilitate this process, before biasing our decision towards a class of neural networks or running an expensive search over the network space, we propose to estimate the classification difficulty of the dataset. Our method computes a single number that characterizes the dataset difficulty 27x faster than training state-of-the-art networks. The proposed method can be used in combination with network topology and hyper-parameter search optimizers to efficiently drive the search towards promising neural-network configurations.

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Florian Scheidegger, Roxana Istrate, Giovanni Mariani, Luca Benini, Costas Bekas, Cristiano Malossi. 2018-03-26. Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning Accuracy. https://arxiv.org/abs/1803.09588

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