Wasserstein GAN

CellStrat > Research/Blog > Wasserstein GAN
19
Dec
Generative Modelling with wGAN (Wasserstein GAN)

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars Last Saturday, our AI Lab team Lead Abdul Azeez presented an extensive hands-on workshop on wGANs or Wasserstein GANs. Overview of wGANs :- A GAN is a pair of neural networks – Generator and Discriminator. The G tries to produce real-like images and D tries to distinguish the generated images from real […]

Posted in: Computer Vision, Deep Learning, Generative Modeling,
Tags: 1-Lipschitz function, Earth-Mover Distance, EM Distance, GAN, generative adversarial network, generative modelling, gradient penalty, Jensen-Shannon divergence, JS Divergence, KL Divergence, Kullback-Leibler divergence, Wasserstein distance, Wasserstein GAN, wGAN, wGAN-GP, wGANs,
12
Dec
Meeting Minutes from CellStrat AI Lab meetup on Saturday 7th Dec 2019 at Bengaluru

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars Minutes from Saturday 7th Dec AI Lab meetup at BLR :- Last Saturday we had fabulous presentations by AI Lab members in BLR. Recommender Systems :- In our Hebbal AI Lab, the day started with a deep session on Recommender Systems by Gurumoorthy Loganathan. Recommender system is a subclass of information filtering […]

Posted in: Computer Vision, Deep Learning, Generative Modeling, Machine Learning, Natural Language Processing, Reinforcement Learning,
Tags: BERT, CycleGAN, GANs, generative modelling, keypoint detection, openpose, optimal policy, product recommendations, Question-Answer Model, recommender systems, reinforcement learning, SQuaD, Wasserstein GAN,