authors: Sylvain Gelly Karol Kurach Marcin Michalski Xiaohua Zhai
abstract: They have proposed a novel approach for generative modeling based on
Rademacher Coin Flipping metrics
conclusion: comparing to this dataset selection method, this proposed approach can result very well.
authors: Su Wang, Greg Durrett, Katrin Erk
abstract: They have proposed validated the improvement of accuracy in semantic plausibility by injecting world knowledge to existing neural network. the model is the combination of neural network(3 layers) and world knowledge.
And this is the world knowledge they used in that paper.
authors: Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, Yu-Gang Jiang
project page: http://bigvid.fudan.edu.cn/pixel2mesh/
abstract: They have proposed the method to produce the triangular mesh 3D shape from a single colour input image. Example is below.
And the strategy they used in this paper called "Coarse-to-Fine" approach, which is starting with rough shape and then curve the sphere to make it intricate.
As you can see, they have improved the accuracy significantly.
Future work: Our method only produces meshes with the same topology as the initial
mesh. Future work involves extending our approach to handle more general case,
such as scene level reconstruction, and learn from multiple images for multi-view reconstruction.