Video Content Swapping Using GAN

Lau, Tingfung; Xu, Sailun; Wang, Xinze

doi:10.48550/arxiv.2111.10916

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This has encouraged the use of GANs for synthetic data generation in broader contexts, in particular in high-energy physics, where in some instances the data generation can be a computational intensive task [37][38][39][40]. In this regard, while GANs were developed for image generation [33], there have been attempts to adapt this approach for other formats, such as tabular data [34], time series [41], video content augmentation [42] and audio synthesis [43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Generating gravitational waveform libraries of exotic compact binaries with deep learning

Freitas¹,

Herdeiro²,

Morais³

et al. 2022

Preprint

View full text Add to dashboard Cite

Current gravitational wave (GW) detections rely on the existence of libraries of theoretical waveforms. Consequently, finding new physics with GWs requires libraries of non-standard models, which are computationally demanding. We discuss how deep learning frameworks can be used to generate new waveforms "learned" from a simulation dataset obtained, say, from numerical relativity simulations. Concretely, we use the WaveGAN architecture of a generative adversarial network (GAN). As a proof of concept we provide this neural network (NN) with a sample of (> 500) waveforms from the collisions of exotic compact objects (Proca stars), obtained from numerical relativity simulations. Dividing the sample into a training and a validation set, we show that after a sufficiently large number of training epochs the NN can produce from 12% to 25% of the synthetic waveforms with an overlapping match of at least 95% with the ones from the validation set. We also demonstrate that a NN can be used to predict the overlapping match score, with 90% of accuracy, of new synthetic samples. These are encouraging results for using GANs for data augmentation and interpolation in the context of GWs, to cover the full parameter space of, say, exotic compact binaries, without the need of intensive numerical relativity simulations.

show abstract