Preserving fairness generalization in deepfake detection
Although effective deepfake detection models have been developed in recent years recent
studies have revealed that these models can result in unfair performance disparities among …
studies have revealed that these models can result in unfair performance disparities among …
Robust covid-19 detection in ct images with clip
In the realm of medical imaging, particularly for COVID-19 detection, deep learning models
face substantial challenges such as the necessity for extensive computational resources, the …
face substantial challenges such as the necessity for extensive computational resources, the …
Robust light-weight facial affective behavior recognition with clip
Human affective behavior analysis aims to delve into human expressions and behaviors to
deepen our understanding of human emotions. Basic expression categories (EXPR) and …
deepen our understanding of human emotions. Basic expression categories (EXPR) and …
Robustly optimized deep feature decoupling network for fatty liver diseases detection
Current medical image classification efforts mainly aim for higher average performance,
often neglecting the balance between different classes. This can lead to significant …
often neglecting the balance between different classes. This can lead to significant …
Uncertainty-Aware Explainable Recommendation with Large Language Models
Providing explanations within the recommendation system would boost user satisfaction and
foster trust, especially by elaborating on the reasons for selecting recommended items …
foster trust, especially by elaborating on the reasons for selecting recommended items …
Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Images
Diffusion models (DMs) have revolutionized image generation, producing high-quality
images with applications spanning various fields. However, their ability to create hyper …
images with applications spanning various fields. However, their ability to create hyper …
Iterative thresholding for non-linear learning in the strong -contamination model
A Rathnashyam, A Gittens - arXiv preprint arXiv:2409.03703, 2024 - arxiv.org
We derive approximation bounds for learning single neuron models using thresholded
gradient descent when both the labels and the covariates are possibly corrupted …
gradient descent when both the labels and the covariates are possibly corrupted …