Search Results for author:
Found 13 papers, 6 papers with code
SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection
Misinformation is a prevalent societal issue due to its potential high risks.
Leveraging Old Knowledge to Continually Learn New Classes in Medical Images
Class-incremental continual learning is a core step towards developing artificial intelligence systems that can continuously adapt to changes in the environment by learning new concepts without forgetting those previously learned.
Explanation-based Data Augmentation for Image Classification
This work proposes a framework that utilizes concept-based explanations to automatically augment the dataset with new images that can cover these under-represented regions to improve the model performance.
Distributional Shifts in Automated Diabetic Retinopathy Screening
Deep learning-based models are developed to automatically detect if a retina image is `referable' in diabetic retinopathy (DR) screening.
Towards Fully Interpretable Deep Neural Networks: Are We There Yet?
Despite the remarkable performance, Deep Neural Networks (DNNs) behave as black-boxes hindering user trust in Artificial Intelligence (AI) systems.
Improving Evidence Retrieval for Automated Explainable Fact-Checking
Automated fact-checking on a large-scale is a challenging task that has not been studied systematically until recently.
Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples
In this paper, we propose a novel method, called \emph{Certification through Adaptation}, that transforms an AT model into a randomized smoothing classifier during inference to provide certified robustness for $\ell_2$ norm without affecting their empirical robustness against adversarial attacks.
Comprehensible Convolutional Neural Networks via Guided Concept Learning
Learning concepts that are consistent with human perception is important for Deep Neural Networks to win end-user trust.
Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples
Among existing uncertainty estimation approaches, Dirichlet Prior Network (DPN) distinctly models different predictive uncertainty types.
Approximate Manifold Defense Against Multiple Adversarial Perturbations
Using adversarial training to defend against multiple types of perturbation requires expensive adversarial examples from different perturbation types at each training step.
Normal Similarity Network for Generative Modelling
Gaussian distributions are commonly used as a key building block in many generative models.
Author-aware Aspect Topic Sentiment Model to Retrieve Supporting Opinions from Reviews
User generated content about products and services in the form of reviews are often diverse and even contradictory.
Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach
User opinions expressed in the form of ratings can influence an individual's view of an item.
