Few-shot Generative Adversarial Networks

The most successful computer vision approaches are based on deep learning architectures, which typically require a large amount of labeled data. This can be impractical or expensive to acquire. Therefore, few-shot learning techniques were proposed to learn new concepts with just one or few annotated examples. However, unsupervised methods such as generative adversarial networks (GANs) still require a huge amount of data to be trained. As such, this project will focus on few-shot learning for GANs. This means that at inference time, the user can input a few images of a class never seen before by the model and the model can generate new images from that class. The proposed project will use a standard

Faculty Supervisor:

Derek Nowrouzezahrai

Student:

Matthew Tesfaldet

Partner:

Element AI

Discipline:

Engineering - computer / electrical

Sector:

Professional, scientific and technical services

University:

McGill University