25 citations · 35 across the 4 of their papers we have counts for
5 papers
Evaluating Co-Creativity using Total Information Flow
Vignesh Gokul, Chris Francis, Shlomo Dubnov
Co-creativity in music refers to two or more musicians or musical agents interacting with one another by composing or improvising music. However, this is a very subjective process…
PosCUDA: Position based Convolution for Unlearnable Audio Datasets
Vignesh Gokul, Shlomo Dubnov
Deep learning models require large amounts of clean data to acheive good performance. To avoid the cost of expensive data acquisition, researchers use the abundant data available o…
Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets
Vaikkunth Mugunthan, Vignesh Gokul, Lalana Kagal +1
Federated Generative Adversarial Network (FedGAN) is a communication-efficient approach to train a GAN across distributed clients without clients having to share their sensitive tr…
DPD-InfoGAN: Differentially Private Distributed InfoGAN
Vaikkunth Mugunthan, Vignesh Gokul, Lalana Kagal +1
Generative Adversarial Networks (GANs) are deep learning architectures capable of generating synthetic datasets. Despite producing high-quality synthetic images, the default GAN ha…
Deep Learning for Skin Lesion Classification
P. Mirunalini, Aravindan Chandrabose, Vignesh Gokul +1
Melanoma, a malignant form of skin cancer is very threatening to life. Diagnosis of melanoma at an earlier stage is highly needed as it has a very high cure rate. Benign and malign…