1 citations · 1 across the 9 of their papers we have counts for
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SAGE: Saliency-Guided Contrastive Embeddings
Colton R. Crum, Christopher Sweet, Adam Czajka
Integrating human perceptual priors into the training of neural networks has been shown to raise model generalization, serve as an effective regularizer, and align models with huma…
Divisive Decisions: Improving Salience-Based Training for Generalization in Binary Classification Tasks
Jacob Piland, Chris Sweet, Adam Czajka
Existing saliency-guided training approaches improve model generalization by incorporating a loss term that compares the model's class activation map (CAM) for a sample's true-clas…
DiffGradCAM: A Class Activation Map Using the Full Model Decision to Solve Unaddressed Adversarial Attacks
Jacob Piland, Chris Sweet, Adam Czajka
Class Activation Mapping (CAM) and its gradient-based variants (e.g., GradCAM) have become standard tools for explaining Convolutional Neural Network (CNN) predictions. However, th…
Saliency-Guided Training for Fingerprint Presentation Attack Detection
Samuel Webster, Adam Czajka
Saliency-guided training, which directs model learning to important regions of images, has demonstrated generalization improvements across various biometric presentation attack det…
Almost Right: Making First-Layer Kernels Nearly Orthogonal Improves Model Generalization
Colton R. Crum, Adam Czajka
Despite several algorithmic advances in the training of convolutional neural networks (CNNs) over the years, their generalization capabilities are still subpar across several perti…