#### Problem addressed: 
Learn to generate sharp high resolution images

#### Summary: 
The authors applied generative adversarial networks (GANs) for image modeling. Instead of learning one model for a high resolution image directly, they learn it in a hierachical way using pyramid decomposition of the image. The image is decomposed to smaller versions that contains all low frequency information and its corresponding high frequency ones. It can also be regarded as a similar idea from lossless compression, where one have both compressed version of the image and the corresponding error so that once the compression is given the image can be reconstructed perfectly. The quantitative and qualitaive performance are impressive, and is so that the best image generative model for high resolution images.

#### Novelty:
Using laplacian pyramid decomposition that enables the generation of sharp high resolution images possible

#### Drawbacks:
Training is stagewise and slow, and levels of decomposition is fixed for all kinds of images.

#### Datasets:
CIFAR-10, STL, SUN

#### Additional remarks:
Presentation video available on cedar server

#### Resources:
Other relevant work to this one: Conditional Generative Adversarial Nets, Conditional generative adversarial nets for convolutional face generation

#### Presenter:
Yingbo Zhou