Beyond One-hot Encoding: lower dimensional target embedding

TL;DR

This paper introduces a low-dimensional target embedding method for CNN training that leverages label relationships, significantly speeding up convergence and maintaining high accuracy compared to traditional one-hot encoding.

Contribution

It proposes using random projections and a normalized eigenrepresentation to embed labels into a low-dimensional space, enhancing training efficiency and accuracy.

Findings

Drastically improved convergence speed on multiple datasets.

Achieved competitive accuracy with low-dimensional embeddings.

Validated the effectiveness of label manifold embeddings in CNN training.

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.