AME: Attention and Memory Enhancement in Hyper-Parameter Optimization

Training Deep Neural Networks (DNNs) is inherently subject to sensitive hyper-parameters and untimely feedbacks of performance evaluation. To solve these two difficulties, an efficient parallel hyper-parameter optimization model is proposed under the framework of Deep Reinforcement Learning (DRL). T...

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Bibliographic Details
Published inProceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 480 - 489
Main Authors Xu, Nuo, Chang, Jianlong, Nie, Xing, Huo, Chunlei, Xiang, Shiming, Pan, Chunhong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2022
Subjects
categorization
grouping and shape analysis
Optimization methods; Recognition: detection
Performance evaluation
Reinforcement learning
retrieval; Segmentation
Search problems
Semantics
Training
Transformers
Visualization
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Summary:Training Deep Neural Networks (DNNs) is inherently subject to sensitive hyper-parameters and untimely feedbacks of performance evaluation. To solve these two difficulties, an efficient parallel hyper-parameter optimization model is proposed under the framework of Deep Reinforcement Learning (DRL). Technically, we develop Attention and Memory Enhancement (AME), that includes multi-head attention and memory mechanism to enhance the ability to capture both the short-term and long-term relationships between different hyper-parameter configurations, yielding an attentive sampling mechanism for searching high-performance configurations embedded into a huge search space. During the optimization of transformer-structured configuration searcher, a conceptually intuitive yet powerful strategy is applied to solve the problem of insufficient number of samples due to the untimely feedback. Experiments on three visual tasks, including image classification, object detection, semantic segmentation, demonstrate the effectiveness of AME.
ISSN:1063-6919
DOI:10.1109/CVPR52688.2022.00057