FX-DARTS:设计以差异化架构搜索和基于的超级网络收缩的拓式不受约束的架构
概括
本研究介绍了灵活的DARTS (FX-DARTS),一种在可微分架构搜索中消除架构约束的方法. FX-DARTS能够发现具有提高灵活性和性能的新型神经网络架构.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 可差分架构搜索 (DARTS) 强加强烈的先验,限制了架构灵活性,并阻碍了自动机器学习 (AutoML) 的进步.
- 现有的先验,虽然有助于优化,但限制了对更强大的神经网络架构的探索.
研究的目的:
- 通过消除对细胞拓学的限制和修改超级网络离散,减少DARTS中的先前约束.
- 引入一个灵活的DARTS (FX-DARTS) 方法,在扩大的搜索空间中保持搜索稳定性.
主要方法:
- 开发了灵活的DARTS (FX-DARTS) 方法.开发了灵活的DARTS (FX-DARTS) 方法.
- 利用基于的超级网络缩小 (ESS) 框架来管理扩大的搜索空间.
- 消除了关于细胞拓学的严格先行规则,并修改了离散机制.
主要成果:
- 在没有严格的先行规则的情况下,FX-DARTS成功地导出了神经架构.
- 该方法尽管增加了架构灵活性,但仍然保持了搜索稳定性.
- 图像分类基准的实验结果显示了竞争性性能-复杂性权衡.
结论:
- 在可微分架构搜索中,FX-DARTS增强了架构灵活性.
- 这种方法可以发现新的,高性能的神经网络架构.
- 这种方法为未来的AutoML研究提供了一个有希望的方向.
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