  Neural networks have demonstrated significant accuracy across various
domains, yet their vulnerability to subtle input alterations remains a
persistent challenge. Conventional methods like data augmentation, while
effective to some extent, fall short in addressing unforeseen corruptions,
limiting the adaptability of neural networks in real-world scenarios. In
response, this paper introduces a novel paradigm known as the Mixture of
Class-Specific Expert Architecture. The approach involves disentangling feature
learning for individual classes, offering a nuanced enhancement in scalability
and overall performance. By training dedicated network segments for each class
and subsequently aggregating their outputs, the proposed architecture aims to
mitigate vulnerabilities associated with common neural network structures. The
study underscores the importance of comprehensive evaluation methodologies,
advocating for the incorporation of benchmarks like the common corruptions
benchmark. This inclusion provides nuanced insights into the vulnerabilities of
neural networks, especially concerning their generalization capabilities and
robustness to unforeseen distortions. The research aligns with the broader
objective of advancing the development of highly robust learning systems
capable of nuanced reasoning across diverse and challenging real-world
scenarios. Through this contribution, the paper aims to foster a deeper
understanding of neural network limitations and proposes a practical approach
to enhance their resilience in the face of evolving and unpredictable
conditions.
