Modern Hopfield Networks meet Encoded Neural Representations -- Addressing Practical Considerations
This work addresses practical challenges in associative memory networks for real-world tasks like image retrieval with natural language queries, representing an incremental improvement over existing methods.
The paper tackled the problem of meta-stable states and limited storage capacity in Modern Hopfield Networks by introducing Hopfield Encoding Networks (HEN), which integrate encoded neural representations to improve pattern separability and reduce meta-stable states, resulting in substantial reduction in meta-stable states and increased storage capacity with perfect recall for a larger number of inputs.
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.