“what-where”代码上的自组织映射,走向完全无监督分类。

IF 1.7 4区 工程技术 Q3 COMPUTER SCIENCE, CYBERNETICS Biological Cybernetics Pub Date : 2023-06-01 DOI:10.1007/s00422-023-00963-y
Luis Sa-Couto, Andreas Wichert
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引用次数: 0

摘要

对无监督学习架构的兴趣一直在上升。除了在生物学上不自然之外,依赖大型标记数据集来获得一个性能良好的分类系统是昂贵的。因此,深度学习社区和更多受生物学启发的模型社区都专注于提出无监督技术,这些技术可以产生足够的隐藏表示,然后可以将其馈送到更简单的监督分类器中。尽管这种方法取得了巨大的成功,但对监督模型的最终依赖仍然存在,这迫使预先知道类的数量,并使系统依赖标签来提取概念。为了克服这一限制,最近有人提出了一项研究,展示了如何将自组织映射(SOM)用作完全无监督分类器。然而,为了取得成功,它需要深度学习技术来生成高质量的嵌入。这项工作的目的是表明,我们可以使用我们之前提出的What-Where编码器与SOM串联,以获得端到端的无监督系统,即Hebbian。这样的系统不需要标签来训练,也不需要事先知道哪些类存在。它可以在线培训,并适应可能出现的新课程。正如在最初的工作中一样,我们使用MNIST数据集进行实验分析,并验证该系统达到了与迄今为止报道的最佳系统相似的精度。此外,我们将分析扩展到更困难的Fashion-MNIST问题,并得出结论,该系统仍然可以执行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Self-organizing maps on "what-where" codes towards fully unsupervised classification.

Interest in unsupervised learning architectures has been rising. Besides being biologically unnatural, it is costly to depend on large labeled data sets to get a well-performing classification system. Therefore, both the deep learning community and the more biologically-inspired models community have focused on proposing unsupervised techniques that can produce adequate hidden representations which can then be fed to a simpler supervised classifier. Despite great success with this approach, an ultimate dependence on a supervised model remains, which forces the number of classes to be known beforehand, and makes the system depend on labels to extract concepts. To overcome this limitation, recent work has been proposed that shows how a self-organizing map (SOM) can be used as a completely unsupervised classifier. However, to achieve success it required deep learning techniques to generate high quality embeddings. The purpose of this work is to show that we can use our previously proposed What-Where encoder in tandem with the SOM to get an end-to-end unsupervised system that is Hebbian. Such system, requires no labels to train nor does it require knowledge of which classes exist beforehand. It can be trained online and adapt to new classes that may emerge. As in the original work, we use the MNIST data set to run an experimental analysis and verify that the system achieves similar accuracies to the best ones reported thus far. Furthermore, we extend the analysis to the more difficult Fashion-MNIST problem and conclude that the system still performs.

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来源期刊
Biological Cybernetics
Biological Cybernetics 工程技术-计算机:控制论
CiteScore
3.50
自引率
5.30%
发文量
38
审稿时长
6-12 weeks
期刊介绍: Biological Cybernetics is an interdisciplinary medium for theoretical and application-oriented aspects of information processing in organisms, including sensory, motor, cognitive, and ecological phenomena. Topics covered include: mathematical modeling of biological systems; computational, theoretical or engineering studies with relevance for understanding biological information processing; and artificial implementation of biological information processing and self-organizing principles. Under the main aspects of performance and function of systems, emphasis is laid on communication between life sciences and technical/theoretical disciplines.
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