We've seen how MAML helps us to find the optimal initial model parameter so that we can generalize it to many other related tasks. We've also seen how MAML is used in supervised and reinforcement learning settings. But how can we apply MAML in an unsupervised learning setting where we don't have labels for our data points? So, we introduce a new algorithm called CACTUS short for Clustering to Automatically Generate Tasks for Unsupervised Model Agnostic Meta Learning.

Let's say we have a dataset containing unlabeled examples: . Now, what can we do with this dataset? How can we apply MAML over this dataset? First, what do we need for training using MAML? We need a distribution over tasks and we train our model by sampling a batch of tasks and find the optimal model parameter. A task should contain a feature along with its label. But how can we generate a task from our unlabeled dataset?

Let's see how can we generate tasks using CACTUS in the next section. Once we generate the tasks, we can plug them easily into the MAML algorithm and find the optimal model parameter.