Paper

OpenReview forum, arXiv page

Code

For CACTUs code that uses model-agnostic meta-learning (MAML), please see this GitHub repo.

For CACTUs code that uses prototypical networks (ProtoNets), please see this GitHub repo.

Credits

The results in this project leveraged six open-source codebases from six prior works.

We used four unsupervised learning methods:

• "Adversarial Feature Learning": paper, code
• "Deep Clustering for Unsupervised Learning of Visual Features": paper, code
• "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets": paper, code
• "Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer": paper, code

as well as two meta-learning methods:

• "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks": paper, code
• "Prototypical Networks for Few-Shot Learning": paper, code