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.
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