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The vulnerability of models to data aberrations and adversarial attacksinfluences their ability to demarcate distinct class boundaries efficiently.The network's confidence and uncertainty play a pivotal role in weightadjustments and the extent of acknowledging such attacks. In this paper, weaddress the trade-off between the accuracy and calibration potential of aclassification network. We study the significance of ground-truth distributionchanges on the performance and generalizability of various state-of-the-artnetworks and compare the proposed method's response to unanticipated attacks.Furthermore, we demonstrate the role of label-smoothing regularization andnormalization in yielding better generalizability and calibrated probabilitydistribution by proposing normalized soft labels to enhance the calibration offeature maps. Subsequently, we substantiate our inference by translatingconventional convolutions to padding based partial convolution to establish thetangible impact of corrections in reinforcing the performance and convergencerate. We graphically elucidate the implication of such variations with thecritical purpose of corroborating the reliability and reproducibility formultiple datasets.

Normalized Label Distribution: Towards Learning Calibrated, Adaptable and Efficient Activation Maps