In this paper, we propose a new iterative hierarchical data augmentation (IHDA) method to fine-tune trained deep neural networks to improve their generalization performance. The IHDA is motivated by three key insights: (1) Deep networks (DNs) are good at learning multi-level representations from data.

DA increases the size of the training data set, generally, in two ways.

In this paper, the authors propose a new method for training deep networks that relies on learning a generative model for data augmentations. The resulting generating model is derived from a variational auto-encoder (VAE) and trained to learn augmentations for each hidden representation in the network. The authors test this new method for data augmentation and fine-tuning on CIFAR-10, CIFAR-100 and ImageNet. On all of these benchmarks, the authors identified substantial gains in terms of classification accuracy. The reviewers raised concerns in terms of the theoretical grounding of the method, the strength of the baselines, the training time in practice and clarity of presentation of the complex method.

In this paper, we propose a new iterative hierarchical data augmentation (IHDA) method to fine-tune trained deep neural networks to improve their generalization performance. The IHDA is motivated by three key insights: (1) Deep networks (DNs) are good at learning multi-level representations from data. Accordingly, the IHDA performs DA in a deep feature space, at level l, by transforming it into a distribution space and synthesizing new samples using the learned distributions for data points that lie in hard-to-classify regions, which is estimated by analyzing the neighborhood characteristics of each data point. The synthesized samples are used to fine-tune the parameters of the subsequent layers. The same procedure is then repeated for the feature space at level l 1.