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In this work, we propose a novel activation mechanism called LayerAct forCNNs. This approach is motivated by our theoretical and experimental analyses,which demonstrate that Layer Normalization (LN) can mitigate a limitation ofexisting activation functions regarding noise robustness. However, LN is knownto be disadvantageous in CNNs due to its tendency to make activation outputshomogeneous. The proposed method is designed to be more robust than existingactivation functions by reducing the upper bound of influence caused by inputshifts without inheriting LN's limitation. We provide analyses and experimentsshowing that LayerAct functions exhibit superior robustness compared toElementAct functions. Experimental results on three clean and noisy benchmarkdatasets for image classification tasks indicate that LayerAct functionsoutperform other activation functions in handling noisy datasets whileachieving superior performance on clean datasets in most cases.

LayerAct: Advanced Activation Mechanism for Robust Inference of CNNs