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Autoencoding With a Classifier System
IEEE Transactions on Evolutionary Computation ( IF 14.3 ) Pub Date : 2021-05-11 , DOI: 10.1109/tevc.2021.3079320
Richard J. Preen , Stewart W. Wilson , Larry Bull

Autoencoders are data-specific compression algorithms learned automatically from examples. The predominant approach has been to construct single large global models that cover the domain. However, training and evaluating models of increasing size comes at the price of additional time and computational cost. Conditional computation, sparsity, and model pruning techniques can reduce these costs while maintaining performance. Learning classifier systems (LCSs) are a framework for adaptively subdividing input spaces into an ensemble of simpler local approximations that together cover the domain. LCS perform conditional computation through the use of a population of individual gating/guarding components, each associated with a local approximation. This article explores the use of an LCS to adaptively decompose the input domain into a collection of small autoencoders, where local solutions of different complexity may emerge. In addition to the benefits in convergence time and computational cost, it is shown possible to reduce the code size as well as the resulting decoder computational cost when compared with the global model equivalent.

中文翻译:

使用分类器系统自动编码

自编码器是从示例中自动学习的特定于数据的压缩算法。主要的方法是构建覆盖该领域的单个大型全局模型。然而,训练和评估越来越大的模型是以增加时间和计算成本为代价的。条件计算、稀疏性和模型修剪技术可以在保持性能的同时降低这些成本。学习分类器系统 (LCS) 是一种框架,用于自适应地将输入空间细分为更简单的局部近似集合,这些集合一起覆盖该域。LCS 通过使用一组单独的门控/保护组件来执行条件计算,每个组件都与一个局部近似值相关联。本文探讨了如何使用 LCS 将输入域自适应地分解为一组小型自编码器,其中可能会出现不同复杂度的局部解决方案。除了收敛时间和计算成本方面的好处外,与等效的全局模型相比,还可以减少代码大小以及由此产生的解码器计算成本。
更新日期:2021-05-11
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