Now we'll see how to learn to learn in the concept space using deep meta learning. First, how do we perform meta learning? We sample a batch of related tasks and some k data points in each task and train our meta learner. Instead of just training using our vanilla meta learning technique, we can combine the power of deep learning with meta learning. So, when we sample a batch of tasks and some k data points in each task, we learn the representations of each of the k data points using deep neural networks and then we'll perform meta learning on those representations.
Our framework consists of three components:
- Concept generator
- Concept discriminator
- Meta learner
The role of the concept generator is to extract the feature representations of each of the data points in our dataset, capturing its high-level concept, and the role of the concept discriminator is to recognize and classify the concepts generated by the concept generator, while the meta learner learns...