We know that, in meta learning, we train the model over a distribution of related tasks so that it can easily be adapted to a new task with only a few samples. In the previous chapters, we've seen how MAML finds the optimal initial parameters of the model by calculating meta gradients and performing meta optimization. But one of the problems we might face is that our model can be biased over some tasks, especially the tasks that are sampled in the meta training phase. So, our model will overperform on these tasks. If the model does so, then it will also lead us to the problem of finding a better update rule. With the biased model over some tasks, we'll also not able to perform better generalization on the unseen tasks that vary greatly from the meta training tasks.

To mitigate this, we need to make our model not get biased or overperform on some of the tasks. That is, we need to make our model task-agnostic, so that we can prevent task bias and attain better generalization. Now, we'll see two algorithms to perform TAML:

• Entropy maximization/reduction
• Inequality minimization