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Neural networks have demonstrated significant accuracy across variousdomains, yet their vulnerability to subtle input alterations remains apersistent challenge. Conventional methods like data augmentation, whileeffective to some extent, fall short in addressing unforeseen corruptions,limiting the adaptability of neural networks in real-world scenarios. Inresponse, this paper introduces a novel paradigm known as the Mixture ofClass-Specific Expert Architecture. The approach involves disentangling featurelearning for individual classes, offering a nuanced enhancement in scalabilityand overall performance. By training dedicated network segments for each classand subsequently aggregating their outputs, the proposed architecture aims tomitigate vulnerabilities associated with common neural network structures. Thestudy underscores the importance of comprehensive evaluation methodologies,advocating for the incorporation of benchmarks like the common corruptionsbenchmark. This inclusion provides nuanced insights into the vulnerabilities ofneural networks, especially concerning their generalization capabilities androbustness to unforeseen distortions. The research aligns with the broaderobjective of advancing the development of highly robust learning systemscapable of nuanced reasoning across diverse and challenging real-worldscenarios. Through this contribution, the paper aims to foster a deeperunderstanding of neural network limitations and proposes a practical approachto enhance their resilience in the face of evolving and unpredictableconditions.

Towards Improving Robustness Against Common Corruptions using Mixture of Class Specific Experts