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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI
Casey Wall, Longwei Wang, Rodrigue Rizk +1
Gradient-weighted Class Activation Mapping (Grad-CAM) is widely used to visualize model decisions, but it was originally formulated for convolutional neural networks, where spatial…
I Detect What I Don't Know: Incremental Anomaly Learning with Stochastic Weight Averaging-Gaussian for Oracle-Free Medical Imaging
Nand Kumar Yadav, Rodrigue Rizk, William CW Chen +1
Unknown anomaly detection in medical imaging remains a fundamental challenge due to the scarcity of labeled anomalies and the high cost of expert supervision. We introduce an unsup…
CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision
Puskal Khadka, Rodrigue Rizk, Longwei Wang +1
Vision Transformers (ViTs) have achieved impressive results in computer vision by leveraging self-attention to model long-range dependencies. However, their emphasis on global cont…
Winsor-CAM: Human-Tunable Visual Explanations from Deep Networks via Layer-Wise Winsorization
Casey Wall, Longwei Wang, Rodrigue Rizk +1
Interpreting Convolutional Neural Networks (CNNs) is critical for safety-sensitive applications such as healthcare and autonomous systems. Popular visual explanation methods like G…