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In this paper we analyze and extend the neural network based associativememory proposed by Gripon and Berrou. This associative memory resembles thecelebrated Willshaw model with an added partite cluster structure. In theliterature, two retrieving schemes have been proposed for the network dynamics,namely sum-of-sum and sum-of-max. They both offer considerably betterperformance than Willshaw and Hopfield networks, when comparable retrievalscenarios are considered. Former discussions and experiments concentrate on theerasure scenario, where a partial message is used as a probe to the network, inthe hope of retrieving the full message. In this regard, sum-of-max outperformssum-of-sum in terms of retrieval rate by a large margin. However, we observethat when noise and errors are present and the network is queried by a corruptprobe, sum-of-max faces a severe limitation as its stringent activation ruleprevents a neuron from reviving back into play once deactivated. In thismanuscript, we categorize and analyze different error scenarios so that boththe erasure and the corrupt scenarios can be treated consistently. We make anamendment to the network structure to improve the retrieval rate, at the costof an extra scalar per neuron. Afterwards, five different approaches areproposed to deal with corrupt probes. As a result, we extend the networkcapability, and also increase the robustness of the retrieving procedure. Wethen experimentally compare all these proposals and discuss pros and cons ofeach approach under different types of errors. Simulation results show that ifcarefully designed, the network is able to preserve both a high retrieval rateand a low running time simultaneously, even when queried by a corrupt probe.

Combating Corrupt Messages in Sparse Clustered Associative Memories