2020/12/12 by Utkarsh Uppal, Uppal, Utkarsh, Bharat Giddwani +1
Computer Science · #Artificial Intelligence (cs.AI) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Machine Learning (cs.LG) #Machine Learning and Data Classification #Video Analysis and Summarization
paper · pdf · doi:10.48550/arxiv.2012.06876
openalex publication_date 2020/12/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.