Adaptive Generative Modeling in Resource-Constrained Environments
Adaptive Generative Modeling in Resource-Constrained Environments
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DOI：
10.23919/date51398.2021.9474046
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发表时间：
2021-02
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影响因子：
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通讯作者：
Jung-Eun Kim;Richard M. Bradford;Max Del Giudice;Zhong Shao
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作者：
Jung-Eun Kim;Richard M. Bradford;Max Del Giudice;Zhong Shao
现代通用技术从不完整或噪声输入中得出现实的数据，需要大量计算这些局限性的限制。为了获得合理的执行成本“回报”。通用建模的设计通过将非循环层引入堆叠的复发架构中，我们可以提高整体效率。
Modern generative techniques, deriving realistic data from incomplete or noisy inputs, require massive computation for rigorous results. These limitations hinder generative techniques from being incorporated in systems in resource-constrained environment, thus motivating methods that grant users control over the time-quality trade-offs for a reasonable “payoff” of execution cost. Hence, as a new paradigm for adaptively organizing and employing recurrent networks, we propose an architectural design for generative modeling achieving flexible quality. We boost the overall efficiency by introducing non-recurrent layers into stacked recurrent architectures. Accordingly, we design the architecture with no redundant recurrent cells so we avoid unnecessary overhead.