HoloGAN (A new generative model) learns 3D representation from natural images

A group of researchers proposed a new generative adversarial network (GAN) using natural images to perform unsupervised learning for 3D representations.

Unlike most GAN model, which depends on 2D kernels to generate images to create blurry images or artifacts in tasks that require a strong 3D understanding, HoloGAN learns from 3D models and realistically showcase this representation.

Paper: https://arxiv.org/pdf/1904.01326.pdf

Github: https://github.com/thunguyenphuoc/HoloGAN

Source: https://www.youtube.com/watch?v=z2DnFOQNECM&feature=youtu.be

Dataset

CelebA:  http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html

LSUN: Dataset and pre-process code  https://github.com/fyu/lsun

ShapeNet Chair: https://drive.google.com/file/d/18GXkDR5Fro8KCldYCcmJXoCEY9iunPME/view?usp=sharing

Cats: Dataset and pre-process code https://github.com/AlexiaJM/RelativisticGAN/tree/master/code

Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.

🐝 Join the Fastest Growing AI Research Newsletter Read by Researchers from Google + NVIDIA + Meta + Stanford + MIT + Microsoft and many others...