Direct Differentiable Augmentation Search

Data augmentation has been an indispensable tool to improve the performance of deep neural networks, however the augmentation can hardly transfer among different tasks and datasets. Consequently, a recent trend is to adopt AutoML technique to learn proper augmentation policy without extensive hand-crafted tuning. In this paper, we propose an efficient differentiable search algorithm called Direct Differentiable Augmentation Search (DDAS). It exploits meta-learning with one-step gradient update and continuous relaxation to the expected training loss for efficient search. Our DDAS can achieve efficient augmentation search without relying on approximations such as Gumbel Softmax or second order gradient approximation. To further reduce the adverse effect of improper augmentations, we organize the search space into a two level hierarchy, in which we first decide whether to apply augmentation, and then determine the specific augmentation policy. On standard image classification benchmarks, our DDAS achieves state-of-the-art performance and efficiency tradeoff while reducing the search cost dramatically, e.g. 0.15 GPU hours for CIFAR-10. In addition, we also use DDAS to search augmentation for object detection task and achieve comparable performance with AutoAugment, while being 1000x faster.

Neural Architecture Search as Sparse Supernet

This paper aims at enlarging the problem of Neural Architecture Search from Single-Path and Multi-Path Search to automated Mixed-Path Search. In particular, we model the new problem as a sparse supernet with a new continuous architecture representation using a mixture of sparsity constraints, i.e., Sparse Group Lasso. The sparse supernet is expected to automatically achieve sparsely-mixed paths upon a compact set of nodes. To optimize the proposed sparse supernet, we exploit a hierarchical accelerated proximal gradient algorithm within a bi-level optimization framework. Extensive experiments on CIFAR-10, CIFAR-100, Tiny ImageNet and ImageNet demonstrate that the proposed methodology is capable of searching for compact, general and powerful neural architectures.