1 code implementation • 7 Mar 2024 • Rashindrie Perera, Saman Halgamuge
In this paper, we look at cross-domain few-shot classification which presents the challenging task of learning new classes in previously unseen domains with few labelled examples.
no code implementations • 4 Mar 2024 • Maneesha Perera, Julian De Hoog, Kasun Bandara, Damith Senanayake, Saman Halgamuge
In this work, we propose two deep-learning-based regional forecasting methods that can effectively leverage both types of time series (aggregated and individual) with weather data in a region.
no code implementations • 6 Jan 2024 • Haihang Wu, Wei Wang, Tamasha Malepathirana, Damith Senanayake, Denny Oetomo, Saman Halgamuge
Neural growth is the process of growing a small neural network to a large network and has been utilized to accelerate the training of deep neural networks.
1 code implementation • 18 Dec 2023 • Nisal Ranasinghe, Damith Senanayake, Sachith Seneviratne, Malin Premaratne, Saman Halgamuge
In this work, we propose GINN-LP, an interpretable neural network to discover the form and coefficients of the underlying equation of a dataset, when the equation is assumed to take the form of a multivariate Laurent Polynomial.
1 code implementation • 18 Aug 2023 • Tamasha Malepathirana, Damith Senanayake, Saman Halgamuge
However, due to the lack of old data, NECIL methods struggle to discriminate between old and new classes causing their feature representations to overlap.
Incremental Learning Non-exemplar-based Class Incremental Learning +1
2 code implementations • 11 May 2023 • Sanjay Saha, Rashindrie Perera, Sachith Seneviratne, Tamasha Malepathirana, Sanka Rasnayaka, Deshani Geethika, Terence Sim, Saman Halgamuge
This paradigm has been under-explored by the current deepfake detection methods in the academic literature.
1 code implementation • ICCV 2023 • Tamasha Malepathirana, Damith Senanayake, Saman Halgamuge
However, due to the lack of old data, NECIL methods struggle to discriminate between old and new classes causing their feature representations to overlap.
Incremental Learning Non-exemplar-based Class Incremental Learning +1
1 code implementation • 22 Jun 2022 • Maneesha Perera, Julian De Hoog, Kasun Bandara, Saman Halgamuge
We propose a forecast combination approach based on particle swarm optimization (PSO) that will enable a forecaster to produce accurate forecasts for the task at hand by weighting the forecasts produced by individual models.
no code implementations • 2 Mar 2022 • Hansani Weeratunge, Zakiya Shireen, Sagar Iyer, Richard Sandberg, Saman Halgamuge, Adrian Menzel, Andrew Phillips, Elnaz Hajizadeh
Here, we report the development of a detailed "Materials Informatics" framework for the design of acoustic coatings for underwater sound attenuation through integrating Machine Learning (ML) and statistical optimization algorithms with a Finite Element Model (FEM).
1 code implementation • 9 Dec 2019 • Damith Senanayake, Wei Wang, Shalin H. Naik, Saman Halgamuge
In addition, SONG is capable of handling new data increments, no matter whether they are similar or heterogeneous to the already observed data distribution.
1 code implementation • ICLR 2019 • Wei Wang, Yuan Sun, Saman Halgamuge
To address this issue, we propose a repulsive loss function to actively learn the difference among the real data by simply rearranging the terms in MMD.
Ranked #19 on Image Generation on STL-10