We propose Deep Feature Interpolation (DFI), a new data-driven baseline for automatic high-resolution image transformation.
To tackle the sentiment classification problem in low-resource languages without adequate annotated data, we propose an Adversarial Deep Averaging Network (ADAN) to transfer the knowledge learned from labeled data on a resource-rich source language to low-resource languages where only unlabeled data exists.
With stochastic depth we can increase the depth of residual networks even beyond 1200 layers and still yield meaningful improvements in test error (4. 91% on CIFAR-10).
Ranked #21 on Image Classification on SVHN
In this paper, we propose a third, alternative approach to combat overfitting: we extend the training set with infinitely many artificial training examples that are obtained by corrupting the original training data.
Empirical evidence suggests that hashing is an effective strategy for dimensionality reduction and practical nonparametric estimation.