Abstract:<div>The slow-convergence problem degrades the segmentation performance of the recently proposed Quantum-Inspired Self-supervised Neural Network models owing to lack of suitable tailoring of the inter-connection weights. Hence, incorporation of quantum-inspired meta-heuristics in the Quantum-Inspired Self-supervised Neural Network models optimizes their hyper-parameters and inter-connection weights. This paper is aimed at proposing an optimized version of a Quantum-Inspired Self-supervised Neural Network… Show more
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