We, the Deep Learning research group of WEBLAB, are developing the future of artificial intelligence technologies. Our activities cover not only researches but also education and development.
We use Virtual Reality as an intuitive interface for Imitation Learning to supervise a deep neural network based visual attention model.
A generative model for faces based on Conditional VAE-GAN. – M. Suzuki
Joint work with The University of Tokyo Hospital. CNN, VAE, Generative Models, etc. – K. Nakayama, et al.
High-performance Stacked denoising Autoencoder enhanced by CrossPre (Cross-Layer Pretraining). – K.Nakayama
We are organising a special interest group on deep learning and several lecture series of deep learning – Deeplearning.jp | AIL.tokyo
To enhance computational performance on deep learning algorithms, we are working on projects such as GPU system virtualization and distributed GPU computation models. We are also providing iLect, a GPU-enabled programming environment accessible by Web broseewsers. – iLect | HPC
Evolutional deep learning algorithms towards flexible networks. – H. Kurotaki
Sensors, CNN, Model Compression for limited resource environment. – Y. Iwasawa
日本語
