How to build a robust ML model using Curriculum learning?

Curriculum learning is also a type of machine learning that trains the model in such a way that humans get trained using their education system

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The learning procedure of every kind of machine learning program is somewhere related to the behaviour and way of learning of humans. For example, an image classification model tells us about the class of the image like a human using the knowledge gained by the humans in the form of the data. Curriculum learning is also a type of machine learning that trains the model in such a way that humans get trained using their education system. In this article, we are going to discuss curriculum learning. The major points to be discussed in the article are listed below. 

Table of content

  1. About curriculum learning
  2. Effectiveness of curriculum learning 
  3. A simple example of curriculum learning
  4. Use cases of curriculum learning     

Let’s start with understanding the curriculum learning 

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About curriculum learning?

Curriculum learning can be considered as a type of machine learning procedure that is inspired by the learning strategy of humans. However, all the other methods of machine learning are also inspired by some of the learning methods of humans but this learning is an approach to making machines learn progressively. As a human, we know that we start to learn using some simple examples and by the time we become able to understand complex examples too. Similarly, this type of learning utilizes simple examples to complex examples to make a machine learn about any task. 

We can also understand this learning as the process of training machine learning models using easier aspects of the task or easier subtasks and after this training increasing the level of complexity gradually. We can take an example of this type of learning from making a machine learn grammatical architectures. At the start, we can push a limited knowledge of grammar into a recurrent neural network but then we can expand resources of learning gradually as the network learns previously given limited knowledge.  This strategy of learning is very important for developmental psychology in machines.       

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Effectiveness of curriculum learning 

In general machine learning processes, we find the existence of large datasets. These datasets are enough for making a machine learn but the problem occurs with the models. If the models are not well parameterized they can represent a higher amount of loss and errors. Modelling whole data in one go can be a very difficult task when we are expecting a higher accuracy from the models. Also, there are issues like slow modelling, failure of convergence, etc. these are all the things we face with the general modelling.

By the above points, we can say that this learning is a process where the data we are providing to the model is gradually increasing with the complexity of examples. When at a point we look at the data we are providing to the model it can consist of a very low amount of examples or samples and here we can see the changes in robustness and convergence of the models. 

Also, the model is already learned using simple examples. There are higher chances of enhancing the accuracy of the model when we are pushing the data gradually. We can understand the effectiveness of this type of learning by taking a simple example of animal training in real life. Applying whole knowledge in one go can make the animal confused but applying knowledge with gradually increasing difficulty can make the animal well trained about any tasks. 

A simple example of curriculum learning

This section will let us know how we can proceed to perform curriculum learning. Let’s say we have prepared a convolutional neural network where the network consists of convolutional layers and a fully connected layer with some neurons and an output layer. This kind of network can become very hard to train using high dimensional data and also can become bounded to provide accuracy till 50 to 60%.  

Let’s consider the CIFAR-10 data. As we know that there are 10 classes of images on the data and for our model, some of the classes are harder to learn. In this scenario, we can train our model using classes that are easy to learn. For example, 6 classes are easily learnable out of 10 classes so we can start training using 6 classes and after this, we can introduce new classes one by one at every new epoch. Finally, after 5 epochs our model will train in every class. This way we can train a machine learning model in the curriculum manner.           

The below image is a representation of the working of a model that uses the curriculum learning method to complete the molecular graph learning.

Image source

The above image is self-explanatory. Still, by looking at it we can take the idea of making blocks of our curriculum learning procedure. 

Use cases of curriculum learning 

There are various use cases of curriculum learning, some of them are as follows:

  • In reinforcement learning, there are five kinds of the curriculum learning program 
  1. Teacher-student curriculum learning
  2. Self-play curriculum learning
  3. Goal generation curriculum learning
  4. Skills extraction curriculum learning
  5. Distillation curriculum learning

By just looking at the type we can understand how important this topic is in the reinforcement learning procedure. The basic approach for all types of curriculum learning is to increase the complexity of learning samples or the environment of any agent.  

  • In chemistry, we find usage of this type of learning in the models that are trying to learn the structures of the molecules and structures of the compounds.     
  • A variety of neural networks designed to perform classification can include curriculum learning as their accuracy booster. 
  • We can also use this type of learning program in our projects when the data is imbalanced. We can call this type of learning continuous learning. 
  • There are various implementations of this type of learning algorithm that can be found in the question-answering industry and industries that help us in improving the grammar of our content.  
  • In the field of natural language processing, we can find the use of curriculum learning to improve the performance of neural machine translation.

Final words 

In the article, we have discussed the curriculum learning that can be considered as a way of modelling to improve the performance of the general models by pushing the data into training by increasing the complexity of the data sample gradually. Along with this, we have discussed the effectiveness, example and use cases of curriculum modelling.            

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Yugesh Verma
Yugesh is a graduate in automobile engineering and worked as a data analyst intern. He completed several Data Science projects. He has a strong interest in Deep Learning and writing blogs on data science and machine learning.

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