通过编码神经表示提升霍普菲尔德网络的存储与检索能力
Modern Hopfield Networks meet Encoded Neural Representations -- Addressing Practical Considerations
- 将编码神经表示融入现代霍普菲尔德网络，增强模式区分度
- 显著减少元稳定态，存储容量大幅提升且实现完美召回
- 支持图像与自然语言跨模态关联检索，适用场景更广
内容可寻址记忆如现代霍普菲尔德网络（MHN）被视作人类陈述性记忆中自联想与存储/检索的数学模型，但其在大规模内容存储中的实际应用仍面临挑战，尤其是高维内容下易出现元稳定态。本文提出霍普菲尔德编码网络（HEN），将编码神经表示引入MHN，以提升模式可分性并减少元稳定态。实验表明，该方法不仅显著降低元稳定态，还大幅提高存储容量，并能实现对大量输入的完美召回。此外，HEN可在图像与自然语言查询的异构关联场景中进行检索，无需依赖同域部分内容，突破了传统限制，提升了关联记忆网络在真实任务中的实用性。
原文摘要 · Abstract (English)
Content-addressable memories such as Modern Hopfield Networks (MHN) have been studied as mathematical models of auto-association and storage/retrieval in the human declarative memory, yet their practical use for large-scale content storage faces challenges. Chief among them is the occurrence of meta-stable states, particularly when handling large amounts of high dimensional content. This paper introduces Hopfield Encoding Networks (HEN), a framework that integrates encoded neural representations into MHNs to improve pattern separability and reduce meta-stable states. We show that HEN can also be used for retrieval in the context of hetero association of images with natural language queries, thus removing the limitation of requiring access to partial content in the same domain. Experimental results demonstrate substantial reduction in meta-stable states and increased storage capacity while still enabling perfect recall of a significantly larger number of inputs advancing the practical utility of associative memory networks for real-world tasks.
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