In the past years, significant improvements in the field of neural architecture search(NAS) have been made. However, it is still challenging to search for efficient networks due to the gap between the searched constraint and real inference time exists. To search for a high-performance network with low inference time, several previous works set a computational complexity constraint for the search algorithm. However, many factors affect the speed of inference(e.g., FLOPs, MACs). The correlation between a single indicator and the latency is not strong. Currently, some re-parameterization(Rep) techniques are proposed to convert multi-branch to single-path architecture which is inference-friendly. Nevertheless, multi-branch architectures are still human-defined and inefficient. In this work, we propose a new search space that is suitable for structural re-parameterization techniques. RepNAS, a onestage NAS approach, is present to efficiently search the optimal diverse branch block(ODBB) for each layer under the branch number constraint. Our experimental results show the searched ODBB can easily surpass the manual diverse branch block(DBB) with efficient training. Code and models will be available sooner.

Previous AutoML pruning works utilized individual layer features to automatically prune filters. We analyze the correlation for two layers from different blocks which have a short-cut structure. It is found that, in one block, the deeper layer has many redundant filters which can be represented by filters in the former layer so that it is necessary to take information from other layers into consideration in pruning. In this paper, a graph pruning approach is proposed, which views any deep model as a topology graph. Graph Prun-ingNet based on the graph convolution network is designed to automatically extract neighboring information for each node. To extract features from various topologies, Graph PruningNet is connected with Pruned Network by an individual fully connection layer for each node and jointly trained on a training dataset from scratch. Thus, we can obtain reasonable weights for any size of sub-network. We then search the best configuration of the Pruned Network by reinforcement learning. Different from previous work, we take the node features from well-trained Graph Prun-ingNet, instead of the hand-craft features, as the states in reinforcement learning. Compared with other AutoML pruning works, our method has achieved the state-of-the-art under same conditions on ImageNet-2012. The code will be released on GitHub.

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