8 citations · 14 across the 4 of their papers we have counts for
4 papers
Dance2Music: Automatic Dance-driven Music Generation
Gunjan Aggarwal, Devi Parikh
Dance and music typically go hand in hand. The complexities in dance, music, and their synchronisation make them fascinating to study from a computational creativity perspective. W…
Neuro-Symbolic Generative Art: A Preliminary Study
Gunjan Aggarwal, Devi Parikh
There are two classes of generative art approaches: neural, where a deep model is trained to generate samples from a data distribution, and symbolic or algorithmic, where an artist…
On the Benefits of Models with Perceptually-Aligned Gradients
Gunjan Aggarwal, Abhishek Sinha, Nupur Kumari +1
Adversarial robust models have been shown to learn more robust and interpretable features than standard trained models. As shown in [\cite{tsipras2018robustness}], such robust mode…
cFineGAN: Unsupervised multi-conditional fine-grained image generation
Gunjan Aggarwal, Abhishek Sinha
We propose an unsupervised multi-conditional image generation pipeline: cFineGAN, that can generate an image conditioned on two input images such that the generated image preserves…