Example-Based Framework for Perceptually Guided Audio Texture Generation

no code implementations • 23 Aug 2023 • Purnima Kamath, Chitralekha Gupta, Lonce Wyse, Suranga Nanayakkara

By using a few synthetic examples to indicate the presence or absence of a semantic attribute, we infer the guidance vectors in the latent space of the StyleGAN to control that attribute during generation.

Attribute Texture Synthesis

VR.net: A Real-world Dataset for Virtual Reality Motion Sickness Research

no code implementations • 6 Jun 2023 • Elliott Wen, Chitralekha Gupta, Prasanth Sasikumar, Mark Billinghurst, James Wilmott, Emily Skow, Arindam Dey, Suranga Nanayakkara

Researchers have used machine learning approaches to identify motion sickness in VR experience.

Towards Controllable Audio Texture Morphing

no code implementations • 23 Apr 2023 • Chitralekha Gupta, Purnima Kamath, Yize Wei, Zhuoyao Li, Suranga Nanayakkara, Lonce Wyse

In this paper, we propose a data-driven approach to train a Generative Adversarial Network (GAN) conditioned on "soft-labels" distilled from the penultimate layer of an audio classifier trained on a target set of audio texture classes.

Generative Adversarial Network

Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering

1 code implementation • 6 Oct 2022 • Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Tharindu Kaluarachchi, Rajib Rana, Suranga Nanayakkara

We propose \textit{RAG-end2end}, an extension to RAG, that can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training.

Domain Adaptation Information Retrieval +3

Fine-tune the Entire RAG Architecture (including DPR retriever) for Question-Answering

1 code implementation • 22 Jun 2021 • Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Suranga Nanayakkara

In this paper, we illustrate how to fine-tune the entire Retrieval Augment Generation (RAG) architecture in an end-to-end manner.

Open-Domain Question Answering Retrieval

Jointly Fine-Tuning “BERT-like” Self Supervised Models to Improve Multimodal Speech Emotion Recognition

1 code implementation • Interspeech 2020 • Shamane Siriwardhana, Andrew Reis, Rivindu Weerasekera, Suranga Nanayakkara

Multimodal emotion recognition from speech is an important area in affective computing.

Multimodal Deep Learning Multimodal Emotion Recognition +4

Jointly Fine-Tuning "BERT-like" Self Supervised Models to Improve Multimodal Speech Emotion Recognition

1 code implementation • 15 Aug 2020 • Shamane Siriwardhana, Andrew Reis, Rivindu Weerasekera, Suranga Nanayakkara

Multimodal emotion recognition from speech is an important area in affective computing.

VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation

2 code implementations • 18 Aug 2019 • Shamane Siriwardhana, Rivindu Weerasakera, Denys J. C. Matthies, Suranga Nanayakkara

In this paper, we show how novel transfer reinforcement learning techniques can be applied to the complex task of target driven navigation using the photorealistic AI2THOR simulator.

reinforcement-learning Reinforcement Learning (RL) +2

Target Driven Visual Navigation with Hybrid Asynchronous Universal Successor Representations

no code implementations • 27 Nov 2018 • Shamane Siriwardhana, Rivindu Weerasekera, Suranga Nanayakkara

Being able to navigate to a target with minimal supervision and prior knowledge is critical to creating human-like assistive agents.

Navigate Visual Navigation