How to Improve Deep Learning Models With Small Datasets

May 2, 2022

How to Improve Deep Learning Models With Small Datasets

Deep learning models are notorious for their appetite for data.

The more data you can give them, the better they perform.

Unfortunately, in most real-life situations, this is not possible. You may not have enough data, or the data may be too expensive to collect.

This blog post will discuss four ways to improve deep learning models without more data.

Why deep learning needs so much data?

Deep learning models are compelling because they can learn complex relationships.

Deep learning models comprise multiple layers. Each layer learns a progressively more complex representation of the data.

The first layer might learn to detect simple patterns, such as edges. The second layer might learn to see patterns of those edges, such as shapes.

The third layer might learn to identify objects made up of those shapes, and so on.

Each layer consists of a series of neurons, and they are connected to every neuron in the previous layer.

All these layers and neurons mean there are a ton of parameters to optimize. So deep learning models have a lot of capacity, which is good. But it also means they are prone to overfitting, which is terrible.

Overfitting is when a model captures too much noise in the training data and fails to generalize to new data.

With enough data, deep learning models can learn to detect very complex relationships. Yet, if you do not have enough data, the deep learning model will not be able to understand these complex relationships.

We must have enough data so that the deep learning model can learn.

But when the odds are not so good to collect more data, we have several techniques to overcome them.

I've included links to some useful books in this article. When you buy something I recommend, I may earn a small commission on qualifying purchases. But it never affects your price.

1. Transfer learning can help train deep learning models with small datasets.

Transfer learning is a machine learning technique where you take a model trained on one problem and use it as a starting point to solve a related but different problem.

For example, you could take a model trained on a large dataset of dog images and use it as a starting point to train a model to identify dog breeds.

The hope is that the features learned by the first model can be reused, saving time and resources.

Related: Transfer Learning: The Highest Leverage Deep Learning Skill You Can Learn

There is no rule of thumb on how different the two applications can be. But, you can use transfer learning even if the original and new datasets are very different.

For example, you could take a model trained on images of cats and use it as a starting point to train a model to identify types of camels. The hope here is that the ability to find out four legs in the first model may help recognize camels.

To learn more about transfer learning, you could refer to Transfer Learning for Natural Language Processing. You may also find Hands-On Transfer Learning with Python helpful if you are a Python programmer.

2. Try data augmentation

Data augmentation is a technique where you take your existing data and generate new, synthetic data.

Related: This Tiny Python Package Creates Huge Augmented Datasets

For example, if you have a dataset of images of dogs, you could use data augmentation to generate new pictures of dogs.

You could do this by randomly cropping images, flipping them horizontally, adding noise, and several other techniques.

Data augmentation is beneficial when you have a small dataset.

By generating new data, you can artificially increase the size of your dataset and give your deep learning model more data to work with.

These lecture notes on deep learning are a great starting point to learning more about data augmentation.

4. Use autoencoders

Autoencoders are a deep learning model to learn low-dimensional data representations.

Autoencoders are beneficial when you have a small dataset because they can learn to compress your data into a lower-dimensional space.

There are many different types of autoencoders. Variational autoencoder (VAE) is a popular one. VAEs are a generative model, which means they can generate new data.

This is beneficial because you can use a VAE to generate new data points similar to your training data. This is a great way to increase the size of your dataset without actually having to collect more data.

Final thoughts

These are just a few techniques to overcome the small data problem.

Of course, the best solution is to collect more data. But, if you're working with a small dataset, these techniques can help you build a deep learning model that can generalize well.

But did you know that traditional models may outperform deep learning models on small datasets?

Related: How to Evaluate if Deep Learning Is Right For You?

It may be better to use a traditional machine learning model such as a support vector machine or a decision tree in some cases. It is essential to experiment with different models and see what works best for your specific problem.

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