5 papers
RankOOD -- Class Ranking-based Out-of-Distribution Detection
Dishanika Denipitiyage, Naveen Karunanayake, Suranga Seneviratne +1
We propose RankOOD, a rank-based Out-of-Distribution (OOD) detection approach based on training a model with the Placket-Luce loss, which is now extensively used for preference ali…
Detecting Content Rating Violations in Android Applications: A Vision-Language Approach
D. Denipitiyage, B. Silva, S. Seneviratne +2
Despite regulatory efforts to establish reliable content-rating guidelines for mobile apps, the process of assigning content ratings in the Google Play Store remains self-regulated…
Detecting and Characterising Mobile App Metamorphosis in Google Play Store
D. Denipitiyage, B. Silva, K. Gunathilaka +4
App markets have evolved into highly competitive and dynamic environments for developers. While the traditional app life cycle involves incremental updates for feature enhancements…
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey
Naveen Karunanayake, Ravin Gunawardena, Suranga Seneviratne +1
Deep neural networks (DNNs) deployed in real-world applications can encounter out-of-distribution (OOD) data and adversarial examples. These represent distinct forms of distributio…
ExCeL : Combined Extreme and Collective Logit Information for Enhancing Out-of-Distribution Detection
Naveen Karunanayake, Suranga Seneviratne, Sanjay Chawla
Deep learning models often exhibit overconfidence in predicting out-of-distribution (OOD) data, underscoring the crucial role of OOD detection in ensuring reliability in prediction…