To obtain high-performance models for your AI / Machine Learning / Deep Learning developments, the quality and quantity of available data are determining factors. However, in some situations, access to data sets may be limited. This is likely to hamper the algorithm training process and compromise the performance of each Deep Learning model.
Data Augmentation was invented to solve this problem. This approach offers two major advantages. Firstly, it increases the size of the data set. Secondly, it helps to diversify its composition, thus improving the model's ability to generalize and respond to a variety of use cases. This article aims to provide detailed explanations and instructions on how to implement Data Augmentation techniques.
How does data augmentation work?
The process of creating this augmented data generally involves several stages:
1. Data selection
First of all, you need to select the dataset on which to apply the data augmentation mechanisms.
2. Defining transformations
3. Application of transformations
4. Integration with dataset
The newly generated data is then integrated into the existing data set to increase its size and diversity. Data Augmentation is generally only applied to the training set, to avoid over-fitting the model to the training data.
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Which data formats are covered by this method?
Data augmentation can be applied in various fields and to a wide variety of data formats, including :
Imaging
In the field of Computer Vision, a photo dataset can benefit from Data Augmentation techniques. These include :
- medical images for disease detection ;
- satellite images for mapping ;
- vehicle images for traffic sign recognition.
Audio
Data Augmentation can also be used for applications such as voice recognition or sound event detection. It can be used to generate variations in frequency, intensity or sound environment.
Textual
Time series
Sequential data, such as financial or meteorological time series, can also benefit from Data Augmentation. By augmenting such data, we can indeed produce variations in trends, seasons or patterns of variation. This can help any Machine Learning / Deep Learning model to better capture the complexity of real data.
What transformations are possible?
Data Augmentation offers a wide range of transformations depending on dataset type and task requirements.
For images
To create new variations, the following transformations can be applied to images:
- rotation ;
- reframing ;
- change in brightness ;
- zoom.
For the text
For text, here are the techniques that can be used to generate additional examples:
- paraphrase;
- replacing words ;
- adding or deleting words.
For audio files
In speech recognition, the following transformations can simulate different acoustic conditions:
- Shifting gears ;
- Tone variation ;
- adding noise.
Finally, for tabular
In tabular data, the most common transformation options are :
- disturbance of numerical values ;
- lOne-Hot encoding for categorical variables ;
- generation of synthetic data by interpolation or extrapolation.
It's important to know how to choose the right transformations to preserve the relevance and meaning of the data. Inappropriate application can compromise data quality and result in poor performance of the Machine Learning or Deep Learning model.
Putting it into perspective: the history of neural networks and data augmentation
The history of neural networks goes back to the beginnings of artificial intelligence, with attempts to model the human brain. Early experiments were limited by the computing power available. Thanks to the technological advances of the last decade, and in particular Deep Learning, neural networks have enjoyed a renaissance.
Today's data preparation methods, and in particular Data Augmentation, have become a pillar of this revival, mimicking neuroplasticity. neuroplasticity enriching training datasets with controlled variations. This relationship between the history of neural networks and Data Augmentation reflects the evolution of machine learning.
It enables modern networks to learn from larger and more diverse datasets. By integrating the history of the neural network with today's data augmentation method, it becomes easier to understand the evolution of artificial intelligence and today's challenges in data collection and processing.
A quick reminder: how does a neural network work?
An artificial neural network operates according to principles inspired by the functioning of the human brain. Composed of several layers of interconnected neurons, each neuron acts as an elementary processing unit. Information flows through these neurons in the form of electrical signals, with weights associated with each connection determining their importance.
During training, these weights are iteratively adjusted to optimize the network's performance on a specific task. At each repetition, the network receives training examples and adjusts its weights to minimize a defined cost function.
During training, data is presented to the network in batches. Each batch is propagated through the network. And the model predictions are compared with the actual labels to calculate the error. Using backpropagation and gradient descent optimization, weights are adjusted to reduce this error.
Once trained, the network can be used to make predictions on new data by simply applying the computational operations learned during training.
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