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An Overview of Meta-Learning

Ashesh Anand
Oct 20, 2022

A good machine learning model frequently has to be trained on a lot of samples. Humans, on the other hand, learn new ideas and abilities considerably more quickly and effectively. The training dataset, algorithm, and algorithm parameters all affect how well a learning model performs.

To determine the optimum performing algorithm and algorithm parameters, numerous tests are needed. Methods for meta-learning assist in identifying them and maximizing the number of experiments. Better forecasts are made faster as a result.

Different machine learning models can use meta-learning (e.g. few-shot learning, reinforcement learning, natural language processing, etc.). By using the outputs and metadata from machine learning algorithms as input, meta-learning algorithms produce predictions. The best predictions from machine learning algorithms can be used by meta-learning algorithms to improve their predictions.

Also Read | Life Cycle of a Machine Learning Model

What is Meta-learning?

In computer science, meta-learning commonly referred to as "learning to learn," is a subset of machine learning. By altering specific components of the learning algorithm in response to the findings of experiments, it is used to enhance the outcomes and performance of a learning algorithm. Researchers can learn more about which algorithms produce the best/better predictions from datasets with the use of meta-learning.

Metadata from learning algorithms is used as input by these algorithms. They then formulate forecasts and offer data regarding the outcome of these learning algorithms. Data about data is what metadata is to non-technical users. For instance, the size, resolution, style, date of creation, and owner of an image can represent its metadata in a learning model.

The most significant difficulty in meta-learning is systematic experiment design.

To create a model, algorithms are directly trained on historical data. The model can then be applied later to forecast output values for fresh examples of input, such as a number or a class label.

Learning Algorithms:

Using new examples of data, generate predictions based on historical data. Algorithms that perform meta-learning gain knowledge from the results of other machine learning algorithms. This indicates that the presence of other learning algorithms that have already been trained on data is necessary for meta-learning.

For instance, supervised meta-learning algorithms learn how to associate target values for classification and regression problems with instances of the output from other learning algorithms (such as predicted numbers or class labels).

Similar to machine learning algorithms, meta-learning algorithms predict a number or class label by using the output of already-existing machine learning algorithms as input.

Meta-Learning Algorithms:

Making a forecast based on the output of learning algorithms and learning from the predictions of other models.

This elevates meta-learning a step above machine learning. If machine learning discovers the most effective way to use data to produce predictions, then meta-learning, or meta-machine learning, discovers the most effective way to employ predictions made by machine learning algorithms.

Also Read | Multi-Modal Learning: Types, Benefits & Examples

How does Meta-Learning work exactly?

Depending on the type and nature of the job task, meta-learning can be done in a variety of ways. On the other hand, a meta-learning job often comprises converting the first network's parameters into those of the second network/optimizer.

There are two training techniques used in meta-learning. The meta-learning model is typically trained after several stages of training on the base model. After the forward, backward, and optimization phases that train the base model, the forward training pass for the optimization model is carried out.

After calculating the meta-loss, the gradients for each meta-parameter are calculated. This changes the meta-parameters of the optimizer in machine learning.

One way to calculate the meta-loss is to finish the forward training run of the original model and then combine the losses that have already been computed.

Many deep learning models have tens of thousands, if not millions, of parameters. Coordinate-sharing is a typical method since building a meta-learner with entirely new parameters would be computationally expensive.

Coordinate-sharing is the process of designing the meta-learner/optimizer so that it only learns one parameter from the underlying model and then clones that parameter in place of all the others. The result is that the parameters of the optimizer are independent of those of the model.

Also Read | Advantages and Disadvantages of Deep Learning

Steps to move ahead with Meta-Learning:

Inclusion of a Learning Algorithm:

To "learn how to learn," a learning algorithm is essential. The learner is used to learn the best parameters and computational methods for a particular assignment. We require such a learning system to perform meta-learning.

Dynamic Inductive Bias:

The set of presumptions a learning system employs when making predictions is known as inductive bias. When the algorithm receives inputs it has never encountered, this occurs.

Bias is built as a function of the learning task in a situation known as dynamic bias induction. This only means that a learner's inductive bias is changed to correspond with a specific task. A dynamic inductive bias can be achieved by changing key elements of the learner. The depiction of the hypothesis or parameters is one of these elements.

Extracting insights from Metadata:

A collection of data called metadata describes other data. It could be described as data about data. It consists of knowledge about earlier learning experiences in the context of meta-learning. This aids in formulating a hypothesis for a fresh undertaking.

Also Read | Top Classification Algorithms Using Python

Insights Can be extracted from Meta Data

Types of Meta-Learning:

Recurrent Meta-Learning Model:

Applying meta-learning strategies to recurrent neural networks and other long short-term memory networks are known as meta-learning. The RNN/LSTM model is trained to progressively learn a dataset as part of this technique, and the trained model is then used as the foundation for a new learner.

The optimization approach used to train the initial model was taken into account by the meta-learner. The meta-inherited learner's parameterization allows it to fast initialize and converge while yet having the flexibility to adapt to changing circumstances.

Also Read | Learning Recurrent Neural Network

Optimizer Meta-Learning:

Meta-learning is frequently used to enhance the performance of a neural network that already exists. To enhance the performance of the base neural network, optimizer meta-learning techniques frequently work by adjusting the hyperparameters of another neural network.

The target network should, as a result, get better at handling the task it is being trained on. The usage of a network to enhance the outcomes of gradient descent is one illustration of a meta-learning optimizer.

Metric Meta-Learning:

Neural networks are used in metric-based meta-learning to check whether a metric is being utilized correctly and whether the network or networks are hitting the intended metric.

Few examples are utilized to train the network and teach it the metric space in metric meta-learning, which is comparable to few-shot learning in this regard. The networks are deemed to be failing if they deviate from the common metric employed across the diverse domain.

Few-Shots Meta-Learning:

A few-shots meta-learning strategy involves developing a deep neural network that can generalize from training datasets to new datasets. Similar to a typical classification problem, few-shot classification involves using whole datasets as the data samples.

The model is trained using a variety of learning tasks and data sets, and it is then tuned for optimum performance using a variety of training tasks and untried data.

A single training sample is divided into several classes using this method. As a result, each training sample or dataset may contain a maximum of two classes for a total of four shots. The entire training work in this situation could be categorized as a 4-shot, 2-class classification problem.

In few-shot learning, the theory is that the network may learn to recognize objects after having only seen a few images. Individual training samples are kept to a minimum. This is similar to how a youngster picks out different objects after only seeing a few photographs. Techniques like one-shot generative models and memory-augmented neural networks have been developed using this method.

Advantages of Meta-learning:

Algorithms for Meta-Learning are frequently used to improve machine learning results. Advantages of Meta-Learning include:

It guarantees improved model prediction efficiency thus Optimization of learning algorithms, such as tuning hyperparameters for optimal outcomes. Thus, a meta-learning algorithm is used to execute this optimization work, which is typically carried out by a human.

It aids algorithms' learning so they can better adapt to changing circumstances

Helps in Finding hints to create stronger learning algorithms

Faster and less expensive training methods

Supporting the use of fewer examples when learning

Limiting the number of essential experiments will hasten the learning process.

Constructing more broad models by learning to solve multiple problems rather than simply one: One model's training on one particular set of data is not the main emphasis of meta-learning.

Also Read | Machine Learning: Advantages and Disadvantages

What is Model Agnostic Meta-Learning (MAML)?

The key to these advancements is Model Agnostic Meta-Learning (MAML). Excellent blog postings discussing meta-learning in general (here and here) and MAML, in particular, can be found everywhere (here and here). The algorithm appears to be advanced rocket science because of the complicated mathematics and dense terminology.

But I want to convey the algorithm's intuitive justification in this blog in a way that even someone with no background in meta-learning can easily grasp. The fundamental principle that has guided machine learning researchers since the field's beginnings is to toss all of your tasks into a neural network, then sit back, unwind, and watch them learn.

Also Read | Machine Learning Bias: Meaning, Types, and Prevention

Meta-learning: How Expressive is it?

The restriction of the search space is one of the drawbacks of Meta-Learning that Neural Architecture Search usually addresses. The starting point for Neural Architecture Searches is a manually created encoding of potential structures. The discoveries that can be made are naturally constrained by this manual encoding. To make the search computable at all, there must be a trade-off.

The augmentation of meta-learning data is also severely hampered. Most data augmentation searches are limited to a set of transformations that the meta-learning controller is capable of doing (even automatically generated domain randomization).

These adjustments could be made to the image's brightness or the simulation's shadow intensity, for example. Combining these controllers with generative models that are capable of examining extremely unique data points is an intriguing prospect for improving the freedom of data augmentation.

Instead of rotating already-existing photographs or making them darker/brighter, these generative models might create entirely new images.

Summing Up:

Opportunities for meta-learning come up in a variety of ways, and they can be embraced by employing a variety of learning strategies. Whether or not we are successful in learning a particular task, each attempt provides us with valuable experience that we can utilize to learn new activities.

Meta-learning is one technique that aids in the pursuit of artificial general intelligence. They bring machine learning and problem-solving more closely in line with human capabilities. The goal of meta-learning is to make it simple for algorithms to both perform and learn better.

For data scientists, this makes the machine learning process simpler. For example, the algorithm can adjust its hyperparameters. A general description of meta-learning has been given.

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An Overview of Meta-Learning

What is Meta-learning?

Learning Algorithms:

Meta-Learning Algorithms:

How does Meta-Learning work exactly?

Steps to move ahead with Meta-Learning:

Inclusion of a Learning Algorithm:

Dynamic Inductive Bias:

Extracting insights from Metadata:

Types of Meta-Learning:

Recurrent Meta-Learning Model:

Optimizer Meta-Learning:

Metric Meta-Learning:

Few-Shots Meta-Learning:

Advantages of Meta-learning:

What is Model Agnostic Meta-Learning (MAML)?

Meta-learning: How Expressive is it?

Summing Up:

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