Beyond Traditional Data Augmentation: The Power of Generative Techniques in Machine Learning
In this article we will try to be aware about a powerful technique to generate the data called Data Augmentation. Data augmentation is a technique which creates a new dataset using the existing data on which your Machine Learning model is being trained.
Nowadays, you can see, people are uploading their information of daily life on the internet very frequently. They go outside and find anything, just click the photo, write the caption on it and upload it on social media. We have been doing this for many years. So, if you are working as a ML engineer and on a computer vision application, you can understand that, we upload the image and even we label it as well. So, there is a lot of data already available out there on the internet, why do we need to generate more data using any automatic process? You might have this question in your mind. Let’s clear it up.
The Importance of Data Augmentation in Machine Learning: Why More Data Isn’t Always the Solution
As we know, AI ML is a data centric field, every single task starts from the dataset in this field. According to one recent survey by Google AI Research Lab, Data scientists spend 60% of total time on ML application development for Data Engineering. Moreover, according to one other survey this number stands at >80%.
Furthermore, to stage the data, from row data to prepare it for ML training engineers have to perform a series of tasks including
Data crawling
Data Cleaning, Storing, Managing
Performing ETL (If needed)
Missing/null values management…
I am not listing all the things here down, but too much engineers have to do which takes a major time period. Which eventually resulted in more time and cost of the product. When it comes to the Vision Data for Computer Vision Application, it would be even challenging. That’s where Data Augmentation Technique comes to the rescue. This technique is not only useful for time and cost management but also many other technical prospects as well. Let’s take a look at it as well.
Technical realiablity : Data Augmentation
- Limited data : One of the most common challenges in machine learning is the limited availability of high-quality, diverse training data. Data augmentation techniques can help address this issue by creating new synthetic data that is similar to the original data, thereby increasing the size and diversity of the training dataset.
- Imbalanced data : In some cases, the distribution of classes or labels in the training data may be imbalanced, with some classes having significantly fewer samples than others. This can lead to poor model performance, especially for the minority classes. Data augmentation can help address this issue by creating new samples of the minority classes, thereby balancing the class distribution in the training data.
- Overfitting : Overfitting occurs when a model learns to fit the training data too closely, resulting in poor generalisation performance on new, unseen data. Data augmentation can help address this issue by introducing random variations into the training data, which can help the model learn more robust and generalizable patterns in the data.
- Domain adaptation : In some cases, the training data may come from a different distribution than the test data, which can result in poor model performance. Data augmentation can help address this issue by creating new samples that are similar to the test data distribution, thereby improving the model’s ability to generalise to new, unseen data.
- Model interpretability : Data augmentation can also be used to improve model interpretability by generating new samples that highlight specific features or attributes of the data, which can help users better understand how the model is making predictions.
We have understood the importance of the Data Augmentation technique and in which all situations we can be sheltered. Let’s understand how we can use this technique to improve our AI based appplication’s performance as well as its development time.
How we can leverage Generative Data Augmentation?
Although we have a smaller dataset, we can target a high performance from our ML model using this technique. We can generate a lot more data using the existing ones..
which is enough for ML training
which has the same semantics
more diverse
and many more. These features are well capable of preventing all above mentioned technical and non technical problems which we have as Data Scientist. Ultimately it will result in cost, time and more human efforts.
Here is the best example of Data Augmentation technique. Data Scientists start with the 10 experiments and by leveraging data augmentation they generate a lot more same semantics and varied data which help them to train a Computer Vision Robot which performs certain tasks with high accuracy. Unbelievable!
I hope you find it well and relevant to your ML application and improve everything from data collection to ML training.
Read more blogs on AI ML on AWS here
Thanks for reading.
Regards,
EagerMinds
