publications | Ashwin De Silva

2023

Prospective Learning: Principled Extrapolation to the Future

Ashwin De Silva, Rahul Ramesh, Lyle Ungar, and 8 more authors

Conference on Lifelong Learning Agents, 2023

arXiv
The value of out-of-distribution data

Ashwin De Silva, Rahul Ramesh, Carey Priebe, and 2 more authors

International Conference on Machine Learning, 2023

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Approximately optimal domain adaptation with Fisher’s Linear Discriminant Analysis

Hayden S Helm, Weiwei Yang, Ashwin De Silva, and 2 more authors

arXiv preprint arXiv:2302.14186, 2023

arXiv

2022

The Value of Out-of-Distribution Data

Ashwin De Silva, Rahul Ramesh, Carey E Priebe, and 2 more authors

NeurIPS Workshop on Distribution Shifts, 2022

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More data helps us generalize to a task. But real datasets can contain out-of-distribution (OOD) data; this can come in the form of heterogeneity such as intra-class variability but also in the form of temporal shifts or concept drifts. We demonstrate a counter-intuitive phenomenon for such problems: generalization error of the task can be a non-monotonic function of the number of OOD samples; a small number of OOD samples can improve generalization but if the number of OOD samples is beyond a threshold, then the generalization error can deteriorate. We also show that if we know which samples are OOD, then using a weighted objective between the target and OOD samples ensures that the generalization error decreases monotonically. We demonstrate and analyze this issue using linear classifiers on synthetic datasets and medium-sized neural networks on CIFAR-10.
The Value of Out-of-Distribution Data

Ashwin De Silva, Rahul Ramesh, Carey E Priebe, and 2 more authors

ECCV Workshop on Out-of-distribution Generalization in Computer Vision, 2022

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More data helps us generalize to a task. But real datasets can contain out-of-distribution (OOD) data; this can come in the form of heterogeneity such as intra-class variability but also in the form of temporal shifts or concept drifts. We demonstrate a counter-intuitive phenomenon for such problems: generalization error of the task can be a non-monotonic function of the number of OOD samples; a small number of OOD samples can improve generalization but if the number of OOD samples is beyond a threshold, then the generalization error can deteriorate. We also show that if we know which samples are OOD, then using a weighted objective between the target and OOD samples ensures that the generalization error decreases monotonically. We demonstrate and analyze this issue using linear classifiers on synthetic datasets and medium-sized neural networks on CIFAR-10.
Deep Discriminative to Kernel Generative Networks for Calibrated Inference

Jayanta Dey, Haoyin Xu, Ashwin De Silva, and 6 more authors

arXiv preprint arXiv:2201.13001, 2022

arXiv
Towards Accurate Cross-Domain in-Bed Human Pose Estimation

Mohamed Afham, Udith Haputhanthri, Jathurshan Pradeepkumar, and 3 more authors

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022

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Human behavioral monitoring during sleep is essential for various medical applications. Majority of the contactless human pose estimation algorithms are based on RGB modality, causing ineffectiveness in in-bed pose estimation due to occlusions by blankets and varying illumination conditions. Long-wavelength infrared (LWIR) modality based pose estimation algorithms overcome the aforementioned challenges; however, ground truth pose generations by a human annotator under such conditions are not feasible. A feasible solution to address this issue is to transfer the knowledge learned from images with pose labels and no occlusions, and adapt it towards real world conditions (occlusions due to blankets). In this paper, we propose a novel learning strategy comprises of two-fold data augmentation to reduce the cross-domain discrepancy and knowledge distillation to learn the distribution of unlabeled images in real world conditions. Our experiments and analysis show the effectiveness of our approach over multiple standard human pose estimation baselines.

2021

A Thickness Sensitive Vessel Extraction Framework for Retinal and Conjunctival Vascular Tortuosity Analysis

Ashwin De Silva, Malsha V. Perera, Navodini Wijethilake, and 3 more authors

2021

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Systemic diseases such as diabetes, hypertension, atherosclerosis are among the leading causes of annual human mortality rate. It is suggested that retinal and conjunctival vascular tortuosity is a potential biomarker for such systemic diseases. Most importantly, it is observed that the tortuosity depends on the thickness of these vessels. Therefore, selective calculation of tortuosity within specific vessel thicknesses is required depending on the disease being analysed. In this paper, we propose a thickness sensitive vessel extraction framework that is primarily applicable for studies related to retinal and conjunctival vascular tortuosity. The framework uses a Convolutional Neural Network based on the IterNet architecture to obtain probability maps of the entire vasculature. They are then processed by a multi-scale vessel enhancement technique that exploits both fine and coarse structural vascular details of these probability maps in order to extract vessels of specified thicknesses. We evaluated the proposed framework on four datasets including DRIVE and SBVPI, and obtained Matthew’s Correlation Coefficient values greater than 0.71 for all the datasets. In addition, the proposed framework was utilized to determine the association of diabetes with retinal and conjunctival vascular tortuosity. We observed that retinal vascular tortuosity (Eccentricity based Tortuosity Index) of the diabetic group was significantly higher (p < .05) than that of the non-diabetic group and that conjunctival vascular tortuosity (Total Curvature normalized by Arc Length) of diabetic group was significantly lower (p < .05) than that of the non-diabetic group. These observations were in agreement with the literature, strengthening the suitability of the proposed framework.
A Joint Convolutional and Spatial Quad-Directional LSTM Network for Phase Unwrapping

Malsha V. Perera, and Ashwin De Silva

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021

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Phase unwrapping is a classical ill-posed problem which aims to recover the true phase from wrapped phase. In this paper, we introduce a novel Convolutional Neural Network (CNN) that incorporates a Spatial Quad-Directional Long Short Term Memory (SQD-LSTM) for phase unwrapping, by formulating it as a regression problem. Incorporating SQD-LSTM can circumvent the typical CNNs’ inherent difficulty of learning global spatial dependencies which are vital when recovering the true phase. Furthermore, we employ a problem specific composite loss function to train this network. The proposed network is found to be performing better than the existing methods under severe noise conditions (Normalized Root Mean Square Error of 1.3% at SNR = 0 dB) while spending a significantly less computational time (0.054s). The network also does not require a large scale dataset during training, thus making it ideal for applications with limited data that require fast and accurate phase unwrapping.

2020

Low-cost Active Dry-Contact Surface EMG Sensor for Bionic Arms

Asma M. Naim, Kithmin Wickramasinghe, Ashwin De Silva, and 3 more authors

IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020

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Surface electromyography (sEMG) is a popular bio-signal used for controlling prostheses and finger gesture recognition mechanisms. Myoelectric prostheses are costly, and most commercially available sEMG acquisition systems are not suitable for real-time gesture recognition. In this paper, a method of acquiring sEMG signals using novel low-cost, active, dry-contact, flexible sensors has been proposed. Since the active sEMG sensor was developed to be used along with a bionic arm, the sensor was tested for its ability to acquire sEMG signals that could be used for real-time classification of five selected gestures. In a study of 4 subjects, the average classification accuracy for real-time gesture classification using the active sEMG sensor system was 85%. The common-mode rejection ratio of the sensor was measured to 59 dB, and thus the sensor’s performance was not substantially limited by its active circuitry. The proposed sensors can be interfaced with a variety of amplifiers to perform fully wearable sEMG acquisition. This satisfies the need for a low-cost sEMG acquisition system for prostheses.
Real-Time Hand Gesture Recognition Using Temporal Muscle Activation Maps of Multi-Channel sEMG Signals

Ashwin De Silva, Malsha V. Perera, Kithmin Wickramasinghe, and 3 more authors

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020

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Accurate and real-time hand gesture recognition is essential for controlling advanced hand prostheses. Surface Electromyography (sEMG) signals obtained from the forearm are widely used for this purpose. Here, we introduce a novel hand gesture representation called Temporal Muscle Activation (TMA) maps which captures information about the activation patterns of muscles in the forearm. Based on these maps, we propose an algorithm that can recognize hand gestures in real-time using a Convolution Neural Network. The algorithm was tested on 8 healthy subjects with sEMG signals acquired from 8 electrodes placed along the circumference of the forearm. The average classification accuracy of the proposed method was 94%, which is comparable to state-of-the-art methods. The average computation time of a prediction was 5.5ms, making the algorithm ideal for the real-time gesture recognition applications.