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Author (up) Pau Rodriguez Lopez; Miguel Angel Bautista; Sergio Escalera; Jordi Gonzalez edit   pdf
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  Title Beyond Oneshot Encoding: lower dimensional target embedding Type Journal Article
  Year 2018 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume 75 Issue Pages 21-31  
  Keywords Error correcting output codes; Output embeddings; Deep learning; Computer vision  
  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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  Area Expedition Conference  
  Notes ISE; HuPBA; 600.098; 602.133; 602.121; 600.119 Approved no  
  Call Number Admin @ si @ RBE2018 Serial 3120  
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