Neural Architecture Search in Embedding Space
This addresses the efficiency and optimization challenges in neural architecture search for researchers and practitioners in machine learning, though it appears incremental as it builds on existing NAS frameworks.
The paper tackled the difficulty of optimizing neural architecture search (NAS) due to noncontinuous and high-dimensional search spaces by proposing NAS in embedding space (NASES), which uses architecture encoders and decoders to enable reinforcement learning in an embedding space, achieving comparable performance to other NAS approaches on CIFAR-10 in less than 3.5 GPU hours.
The neural architecture search (NAS) algorithm with reinforcement learning can be a powerful and novel framework for the automatic discovering process of neural architectures. However, its application is restricted by noncontinuous and high-dimensional search spaces, which result in difficulty in optimization. To resolve these problems, we proposed NAS in embedding space (NASES), which is a novel framework. Unlike other NAS with reinforcement learning approaches that search over a discrete and high-dimensional architecture space, this approach enables reinforcement learning to search in an embedding space by using architecture encoders and decoders. The current experiment demonstrated that the performance of the final architecture network using the NASES procedure is comparable with that of other popular NAS approaches for the image classification task on CIFAR-10. The results of the experiment were efficient and indicated that NASES was highly efficient to discover final architecture only in $<$3.5 GPU hours. The beneficial-performance and effectiveness of NASES was impressive when the architecture-embedding searching and weight initialization were applied.