7 citations · 18 across the 8 of their papers we have counts for
11 papers
Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data
Dumindu Tissera, Omar Awadallah, Muhammad Umair Danish +2
Multi-label Classification (MLC) assigns an instance to one or more non-exclusive classes. A challenge arises when the dataset contains a large proportion of instances with no assi…
Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks
Yohannis Kifle Telila, Damitha Senevirathne, Dumindu Tissera +3
Anomaly detection is crucial in the energy sector to identify irregular patterns indicating equipment failures, energy theft, or other issues. Machine learning techniques for anoma…
Forensic Video Analytic Software
Anton Jeran Ratnarajah, Sahani Goonetilleke, Dumindu Tissera +2
Law enforcement officials heavily depend on Forensic Video Analytic (FVA) Software in their evidence extraction process. However present-day FVA software are complex, time consumin…
Moving Object Based Collision-Free Video Synopsis
Anton Jeran Ratnarajah, Sahani Goonetilleke, Dumindu Tissera +2
Video synopsis, summarizing a video to generate a shorter video by exploiting the spatial and temporal redundancies, is important for surveillance and archiving. Existing trajector…
Neural Mixture Models with Expectation-Maximization for End-to-end Deep Clustering
Dumindu Tissera, Kasun Vithanage, Rukshan Wijesinghe +4
Any clustering algorithm must synchronously learn to model the clusters and allocate data to those clusters in the absence of labels. Mixture model-based methods model clusters wit…
End-To-End Data-Dependent Routing in Multi-Path Neural Networks
Dumindu Tissera, Rukshan Wijessinghe, Kasun Vithanage +3
Neural networks are known to give better performance with increased depth due to their ability to learn more abstract features. Although the deepening of networks has been well est…
Transferring Domain Knowledge with an Adviser in Continuous Tasks
Rukshan Wijesinghe, Kasun Vithanage, Dumindu Tissera +3
Recent advances in Reinforcement Learning (RL) have surpassed human-level performance in many simulated environments. However, existing reinforcement learning techniques are incapa…
Anomaly Detection using Deep Reconstruction and Forecasting for Autonomous Systems
Nadarasar Bahavan, Navaratnarajah Suman, Sulhi Cader +9
We propose self-supervised deep algorithms to detect anomalies in heterogeneous autonomous systems using frontal camera video and IMU readings. Given that the video and IMU data ar…
Feature-Dependent Cross-Connections in Multi-Path Neural Networks
Dumindu Tissera, Kasun Vithanage, Rukshan Wijesinghe +3
Learning a particular task from a dataset, samples in which originate from diverse contexts, is challenging, and usually addressed by deepening or widening standard neural networks…
Context-Aware Multipath Networks
Dumindu Tissera, Kumara Kahatapitiya, Rukshan Wijesinghe +2
Making a single network effectively address diverse contexts---learning the variations within a dataset or multiple datasets---is an intriguing step towards achieving generalized i…
Context-Aware Automatic Occlusion Removal
Kumara Kahatapitiya, Dumindu Tissera, Ranga Rodrigo
Occlusion removal is an interesting application of image enhancement, for which, existing work suggests manually-annotated or domain-specific occlusion removal. No work tries to ad…