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arXiv · 1806.10805

Beyond One-hot Encoding: lower dimensional target embedding

Pau Rodríguez·Miguel A. Bautista·Jordi Gonzàlez·Sergio Escalera
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Abstract

Target encoding plays a central role when learning Convolutional Neural Networks. In this realm, One-hot encoding is the most prevalent strategy due to its simplicity. However, this so widespread encoding schema assumes a flat label space, thus ignoring rich relationships existing among labels that can be exploited during training. In large-scale datasets, data does not span the full label space, but instead lies in a low-dimensional output manifold. Following this observation, we embed the targets into a low-dimensional space, drastically improving convergence speed while preserving accuracy. Our contribution is two fold: (i) We show that random projections of the label space are a valid tool to find such lower dimensional embeddings, boosting dramatically convergence rates at zero computational cost; and (ii) we propose a normalized eigenrepresentation of the class manifold that encodes the targets with minimal information loss, improving the accuracy of random projections encoding while enjoying the same convergence rates. Experiments on CIFAR-100, CUB200-2011, Imagenet, and MIT Places demonstrate that the proposed approach drastically improves convergence speed while reaching very competitive accuracy rates.

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Pau Rodríguez, Miguel A. Bautista, Jordi Gonzàlez, Sergio Escalera. 2018-06-28. Beyond One-hot Encoding: lower dimensional target embedding. https://doi.org/10.1016/j.imavis.2018.04.004

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