Model-Agnostic Meta-Learning


Model-Agnostic Meta-Learning (MAML) is a model and algorithm in the field of artificial intelligence. It tries to solve the inadequacies of the gradient-descent method and provide better weight initialization for every new task. It may be directly implemented to any learning problem and model.

Methods like meta-learning subsidize the quest to attain artificial general intelligence. They change artificial intelligence closer to matching how humans learn and solve problems. This also marks the machine learning process as informal for data scientists.

The aim of the trained model is to rapidly learn a new task from a small amount of new data in meta-learning. The model is qualified by the meta-learner to be able to learn on a big number of diverse tasks. In this article, we will understand the key logic behind the deep learning neural network model that is known as the model-agnostic meta-learning model.


The main idea of this method is to train the models’ parameters with a different dataset. The model offers well performance with present reset parameters to fine-tune the architecture over one or more gradient steps when using it for a new task.

This is a technique of training a model’s parameters. Therefore, insufficient gradient steps may optimize the loss function can also be observed, from a feature-learning standpoint because they build an internal representation. We select a generic model’s architecture in this method. As it may be used for several tasks. The main contribution of MAML is a simple model- and task-agnostic fast learning algorithm.

The logic behind MAML

The key objective of MAML is to make available a good initialization of a model’s parameters so as to attain optimal fast learning on a new task with fewer gradient steps. It similarly tries to avoid overfitting scenarios that occur while training a neural network with fewer data architecture. The below figure is an illustration of MAML:


Model-Agnostic Meta-Learning

Look at the model represented by a parameterized function with parameters. We can see in the above diagram;

  • The θ is the model’s parameter.
  • The bold black line is the meta-learning phase.
  • Let’s take up that we have three different new tasks.
  • A gradient step is taken for each task as the gray lines with the arrowheads.
  • We may understand that the parameters, θ, are close to all three optimal parameters of the three tasks.
  • Those make θ the best parameter initialization.
  • That may rapidly adapt to different new tasks.
  • Consequently, only a minor change in the parameters, θ, will lead to an optimal minimization of the loss function of any task.
  • MAML proposes that we should first learn θ over the primary dataset following this observation.
  • We only charge a small step whereas fine-tuning on the real dataset.

Model-Agnostic Meta-Learning

MAML Application

  • Robots require to take appropriate data comprising of information about kinesthetic variations when it comes to artificial learning.
  • That means awareness about their body parts movements, control, and other kinds of input.
  • Alternatively, a human brain may learn just by viewing some videos.
  • DAML tried to solve the problem of artificial learning by using meta-learning.
  • It planned a system for learning robotic operation skills from a single video of humans, by just leveraging robust priors.
  • DAML offered a behavior-cloning objective, temporal loss because robots can’t be trained using imitation-learning loss functions.
  • That similarly acts as a regularization term in log space.
  • We understand that taking robust regularization is essential in any scenario to avoid overfitting.
  • That is particularly happening in the case of one-shot learning.