  The vulnerability of models to data aberrations and adversarial attacks
influences their ability to demarcate distinct class boundaries efficiently.
The network's confidence and uncertainty play a pivotal role in weight
adjustments and the extent of acknowledging such attacks. In this paper, we
address the trade-off between the accuracy and calibration potential of a
classification network. We study the significance of ground-truth distribution
changes on the performance and generalizability of various state-of-the-art
networks and compare the proposed method's response to unanticipated attacks.
Furthermore, we demonstrate the role of label-smoothing regularization and
normalization in yielding better generalizability and calibrated probability
distribution by proposing normalized soft labels to enhance the calibration of
feature maps. Subsequently, we substantiate our inference by translating
conventional convolutions to padding based partial convolution to establish the
tangible impact of corrections in reinforcing the performance and convergence
rate. We graphically elucidate the implication of such variations with the
critical purpose of corroborating the reliability and reproducibility for
multiple datasets.
