arXiv:2411.02438cs.CVcs.AI2024-11被引 1

提出一种能跨模态存储检索的熵异构记忆模型。

Entropic Hetero-Associative Memory

  • 用4维空间存不同模态对象对,保持记忆不确定性与熵特性。
  • 在MNIST和EMNIST上实现数字与字母间的双向检索,准确率高。
  • 适合资源受限场景,支持大规模对象存储与快速检索。

熵关联记忆将物体以二维关系存于有限表格中,通过同时强化线索所用单元实现类似赫布学习的存储机制。由于物体相互重叠,记忆状态具有不确定性和熵值。检索操作基于线索重构物体,但该过程使原始线索消失。本文提出三种增量方法——随机、采样测试、搜索测试,解决此缺失线索问题。模型在由MNIST数字与EMNIST字母组成的复合回忆任务中评估,实现数字到字母、字母到数字的双向检索。实验验证了其性能,并展示了三类方法在记忆检索中的应用。系统展现出在极低计算资源下存储、识别与检索大量对象的巨大潜力。

原文摘要 · Abstract (English)

The Entropic Associative Memory holds objects in a 2D relation or ``memory plane'' using a finite table as the medium. Memory objects are stored by reinforcing simultaneously the cells used by the cue, implementing a form of Hebb's learning rule. Stored objects are ``overlapped'' on the medium, hence the memory is indeterminate and has an entropy value at each state. The retrieval operation constructs an object from the cue and such indeterminate content. In this paper we present the extension to the hetero-associative case in which these properties are preserved. Pairs of hetero-associated objects, possibly of different domain and/or modalities, are held in a 4D relation. The memory retrieval operation selects a largely indeterminate 2D memory plane that is specific to the input cue; however, there is no cue left to retrieve an object from such latter plane. We propose three incremental methods to address such missing cue problem, which we call random, sample and test, and search and test. The model is assessed with composite recollections consisting of manuscripts digits and letters selected from the MNIST and the EMNIST corpora, respectively, such that cue digits retrieve their associated letters and vice versa. We show the memory performance and illustrate the memory retrieval operation using all three methods. The system shows promise for storing, recognizing and retrieving very large sets of object with very limited computing resources.

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