Hardware-Aware Low-Light Image Enhancement via One-Shot Neural Architecture Search with Shrinkage Sampling

Yao, Yuansheng; Liu, Risheng; Zhang, Jiaao; Zhong, Wei; Fan, Xin; Luo, Zhongxuan

doi:10.1109/icme51207.2021.9428092

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…However, such methods need to consume hundreds of GPUs for their search training. To reduce the search training time of traditional NAS methods, researchers have proposed a one-shot neural architecture search strategy [20]. For example, Brock et al (2017) [15] proposed a SMASH model based on one-shot neural architecture search, which generates suboptimal weights by introducing an auxiliary network.…”

Section: Neural Architecture Searchmentioning

confidence: 99%

Skeleton-Based Human Action Recognition Based on Single Path One-Shot Neural Architecture Search

Jiang

Wang

et al. 2023

Electronics

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Skeleton-based human action recognition based on Neural Architecture Search (NAS.) adopts a one-shot NAS strategy. It improves the speed of evaluating candidate models in the search space through weight sharing, which has attracted significant attention. However, directly applying the one-shot NAS method for skeleton recognition requires training a super-net with a large search space that traverses various combinations of model parameters, which often leads to overly large network models and high computational costs. In addition, when training this super-net, the one-shot NAS needs to traverse the entire search space of the complete skeleton recognition task. Furthermore, the traditional method does not consider the optimization of the search strategy. As a result, a significant amount of search time is required to obtain a better skeleton recognition network model. A more efficient weighting model, a NAS skeleton recognition model based on the Single Path One-shot (SNAS-GCN) strategy, is proposed to address the above challenges. First, to reduce the model search space, a simplified four-category search space is introduced to replace the mainstream multi-category search space. Second, to improve the model search efficiency, a single-path one-shot approach is introduced, through which the model randomly samples one architecture at each step of the search training optimization. Finally, an adaptive Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is proposed to obtain a candidate structure of the perfect model automatically. With these three steps, the entire network architecture of the recognition model (and its weights) is fully and equally trained significantly. The search and training costs will be greatly reduced. The search-out model is trained by the NTU-RGB + D and Kinetics datasets to evaluate the performance of the proposed model’s search strategy. The experimental results show that the search time of the proposed method in this paper is 0.3 times longer than that of the state-of-the-art method. Meanwhile, the recognition accuracy is roughly comparable compared to that of the SOTA NAS-GCN method.

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Section: Neural Architecture Searchmentioning

confidence: 99%

Skeleton-Based Human Action Recognition Based on Single Path One-Shot Neural Architecture Search

Jiang

Wang

et al. 2023

Electronics

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“…As the research of intelligent diagnosis methods deepens, the network structure is becoming more and more complex, and selecting the appropriate network model often requires a lot of labor and time costs. Therefore, researchers began to explore automatic methods for building deep learning models based on different tasks [ 13 , 14 , 15 ]. Currently, reinforcement learning is widely used in this approach due to its powerful autonomous decision-making capabilities.…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Ensemble Learning Method for Rolling Bearing Fault Diagnosis under Variable Work Conditions

Li,

Zhang,

Wang

et al. 2024

Sensors

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Ensuring the smooth operation of rolling bearings requires a precise fault diagnosis. Particularly, identifying fault types under varying working conditions holds significant importance in practical engineering. Thus, we propose a reinforcement ensemble method for diagnosing rolling bearing faults under varying working conditions. Firstly, a reinforcement model was designed to select the optimal base learner. Stratified random sampling was used to extract four datasets from raw training data. The reinforcement model was trained by these four datasets, respectively, and we obtained four optimal base learners. Then, a sparse ANN was designed as the ensemble model and the reinforcement learning model that can successfully identify the fault type under variable work conditions was constructed. Extensive experiments were conducted, and the results demonstrate the superiority of the proposed method over other intelligent approaches, with significant practical engineering benefits.

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Hardware-Aware Low-Light Image Enhancement via One-Shot Neural Architecture Search with Shrinkage Sampling

Cited by 2 publications

References 11 publications

Skeleton-Based Human Action Recognition Based on Single Path One-Shot Neural Architecture Search

Skeleton-Based Human Action Recognition Based on Single Path One-Shot Neural Architecture Search

A Reinforcement Ensemble Learning Method for Rolling Bearing Fault Diagnosis under Variable Work Conditions

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