- Abstract: Deep learning methods perform well in various tasks. However, the over-fitting problem remains, where the performance decreases for unknown data. We here provide a novel method named MixFeat, which directly makes the latent space discriminative. MixFeat mixes two feature maps in each latent space and uses one of their labels for learning. We report improved results obtained using existing network models with MixFeat on CIFAR-10/100 datasets. In addition, we show that MixFeat effectively reduces the over-fitting problem even in the case that the training dataset is small or contains errors. We argue that MixFeat is complementary with existing methods that mix both images and labels, in that MixFeat is suitable for discrimination tasks while existing methods are suitable for regression tasks. MixFeat is easy to implement and can be added to various network models without additional computational cost in the inference phase.
- Keywords: regularization, generalization, image classification, latent space, feature learning
- TL;DR: We provide a novel method named MixFeat, which directly makes the latent space discriminative.