111 citations · 230 across the 35 of their papers we have counts for
12 papers · 1 filter
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…
Bridging Symmetry and Robustness: On the Role of Equivariance in Enhancing Adversarial Robustness
Longwei Wang, Ifrat Ikhtear Uddin, KC Santosh +3
Adversarial examples reveal critical vulnerabilities in deep neural networks by exploiting their sensitivity to imperceptible input perturbations. While adversarial training remain…
Toward Carbon-Neutral Human AI: Rethinking Data, Computation, and Learning Paradigms for Sustainable Intelligence
KC Santosh, Rodrigue Rizk, Longwei Wang
The rapid advancement of Artificial Intelligence (AI) has led to unprecedented computational demands, raising significant environmental and ethical concerns. This paper critiques t…
Promoting Shape Bias in CNNs: Frequency-Based and Contrastive Regularization for Corruption Robustness
Robin Narsingh Ranabhat, Longwei Wang, Amit Kumar Patel +1
Convolutional Neural Networks (CNNs) excel at image classification but remain vulnerable to common corruptions that humans handle with ease. A key reason for this fragility is thei…
Expert-Guided Explainable Few-Shot Learning for Medical Image Diagnosis
Ifrat Ikhtear Uddin, Longwei Wang, KC Santosh
Medical image analysis often faces significant challenges due to limited expert-annotated data, hindering both model generalization and clinical adoption. We propose an expert-guid…
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…