no code implementations • 24 Apr 2024 • Charika De Alvis, Suranga Seneviratne
We also provide a quantitative comparison of the performance of different SOTA methods and conclude the survey by discussing the remaining challenges and future research direction.
no code implementations • 8 Apr 2024 • Naveen Karunanayake, Ravin Gunawardena, Suranga Seneviratne, Sanjay Chawla
Finally, we highlight the limitations of the existing work and propose promising research directions that explore adversarial and OOD inputs within a unified framework.
no code implementations • 4 Dec 2023 • Charika De Alvis, Dishanika Denipitiyage, Suranga Seneviratne
We further demonstrate that the performance of RCL as a standalone loss also achieves state-of-the-art level accuracy.
no code implementations • 23 Nov 2023 • 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 predictions.
1 code implementation • Elsevier Computer Networks Journal 2023 • Thilini Dahanayaka, Yasod Ginige, Yi Huang, Guillaume Jourjon, Suranga Seneviratne
First, we show that a well-regularized deep learning model improves the open-set classification and then we propose a novel open-set classification method with three variants that perform consistently over multiple datasets.
no code implementations • 24 Oct 2022 • Oshan Jayawardana, Fariza Rashid, Suranga Seneviratne
Behaviour biometrics are being explored as a viable alternative to overcome the limitations of traditional authentication methods such as passwords and static biometrics.
no code implementations • 8 Dec 2020 • Sicong Wang, Naveen Karunanayake, Tham Nguyen, Suranga Seneviratne
Spam classification over encrypted emails enables the classifier to classify spam email without accessing the email, hence protects the privacy of email content.
no code implementations • 2 Jun 2020 • Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume Jourjon
We show that a novel approach of combining content embeddings and style embeddings outperforms the baseline methods for image similarity such as SIFT, SURF, and various image hashing methods.
no code implementations • 7 May 2020 • Jia-Wei Zhao, Rahat Masood, Suranga Seneviratne
Network security has become an area of significant importance more than ever as highlighted by the eye-opening numbers of data breaches, attacks on critical infrastructure, and malware/ransomware/cryptojacker attacks that are reported almost every day.
no code implementations • 27 Dec 2019 • Yining Hu, Suranga Seneviratne, Kanchana Thilakarathna, Kensuke Fukuda, Aruna Seneviratne
Bitcoin is by far the most popular crypto-currency solution enabling peer-to-peer payments.
1 code implementation • 26 Nov 2019 • Hirunima Jayasekara, Vinoj Jayasundara, Mohamed Athif, Jathushan Rajasegaran, Sandaru Jayasekara, Suranga Seneviratne, Ranga Rodrigo
Capsule networks excel in understanding spatial relationships in 2D data for vision related tasks.
5 code implementations • CVPR 2019 • Jathushan Rajasegaran, Vinoj Jayasundara, Sandaru Jayasekara, Hirunima Jayasekara, Suranga Seneviratne, Ranga Rodrigo
Capsule Network is a promising concept in deep learning, yet its true potential is not fully realized thus far, providing sub-par performance on several key benchmark datasets with complex data.
3 code implementations • 17 Apr 2019 • Vinoj Jayasundara, Sandaru Jayasekara, Hirunima Jayasekara, Jathushan Rajasegaran, Suranga Seneviratne, Ranga Rodrigo
Our system is useful in character recognition for localized languages that lack much labeled training data and even in other related more general contexts such as object recognition.
Ranked #4 on Image Classification on EMNIST-Letters
no code implementations • 26 Apr 2018 • Jathushan Rajasegaran, Suranga Seneviratne, Guillaume Jourjon
We show that further performance increases can be achieved by combining style embeddings with content embeddings.
no code implementations • 22 Sep 2017 • Jagmohan Chauhan, Suranga Seneviratne, Yining Hu, Archan Misra, Aruna Seneviratne, Youngki Lee
Increasing popularity of IoT devices makes a strong case for implementing RNN based inferences for applications such as acoustics based authentication, voice commands, and edge analytics for smart homes.