Review for NeurIPS paper: Post-training Iterative Hierarchical Data Augmentation for Deep Networks

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.