Building high-performing AI and ML models starts with one critical element: the quality of labelled datasets. While accurate and diverse datasets are crucial for reliable outcomes, acquiring them can be both labour-intensive and costly. Data augmentation, which encompasses a range of methods for creating new data points from existing datasets, has emerged as a viable method for improving the quality of labelled data. Let’s learn more about data augmentation and how it improves training data quality for AI/ML models.
Role of Data Augmentation in Improving Labelled Data Quality
Data augmentation enhances the diversity and quality of labelled datasets by introducing variations. These techniques improve the ability of AI/ML models to generalise across different scenarios, making these models more robust and accurate. To understand the impact of data augmentation, let’s explore how it addresses various challenges.
Addresses Data Scarcity
AI/ML models require huge volumes of high-quality data for training. However, acquiring such data can often be challenging due to limited availability or difficulties in accessing and labelling it. When you have limited labelled data for AI/ML, a few data augmentation techniques, such as rotations, scaling, and noise addition, can help you generate additional training datasets.
Prevents Overfitting and Underfitting of AI/ML Models
Enhancing dataset quality with augmentation is also helpful in ensuring model fit. By creating diverse variations from existing data, augmentation prevents the model from memorising specific patterns. A large and varied dataset also reduces reliance on individual or limited data samples, making it difficult for the model to overfit the training set.
Adds More Context and Semantics with Cross-Modal Augmentations
In cross-modal augmentation, information from other modalities—such as text, graphics, and audio—is combined to provide new data, which further adds depth, semantics, and context to databases. For example, synthesising audio from text and adding text descriptions to image datasets can improve these models’ ability to learn cohesive patterns.
Improves Model Robustness
By adding variety and depth, data augmentation enhances labelled datasets, directly contributing to improved model robustness. Techniques like pitch shifting in audio data or brightness adjustments in video data create diverse training samples, enabling AI/ML models to generalise better and perform reliably across varied real-world scenarios.
Proven Data Augmentation Strategies for Model Training
There are few commonly used traditional and advanced data augmentation strategies for training data. First, let’s understand how these two approaches differ.
- Traditional Data Augmentation Techniques: These techniques are direct transformations of existing labelled data for AI/ML. This is done through rule-based programming, using libraries/modules like OpenCV, TensorFlow, etc., or augmentation tools & platforms like AugLy.
- Advanced Data Augmentation: This involves using refined AI algorithms and ML models to generate or modify intricate data with more variability and added context.
Text Data
Traditional Text Data Augmentation Techniques:
- Synonym Replacement: This technique deals with replacing words with synonyms to create different variations while preserving the right meaning.
- Shuffling Word Order: This technique considers rearranging or shuffling words in a sentence to add more diversity. However, every sentence can be rearranged, but only where grammar allows.
- Text Truncation: This involves cutting sentences to mimic incomplete text or summary inputs.
- Random Insertion/Deletion: Modifying phrase structure by adding or deleting non-essential words in a sentence.
Advanced Text Data Augmentation Techniques:
- Contextual Embeddings: This method generates context-relevant paraphrases using language models like BERT.
- Back Translation: Translation of text to another language and back for creating new sentence variations.
- Adversarial Text Examples: This involves introducing minor changes only, such as typos, homophones, or case shifts, just to test the robustness of the AI/ML model.
- Sentence Fusion: This technique works by combining two or more sentences to create multiple inputs.
Image Data
Traditional Image Data Augmentation Techniques:
- Geometric Transformations: Cropping, rotating, and scaling for variability in the size and orientation of the image.
- Blurring or Sharpening: Adjusting the sharpness of images to prepare models for adding variations in focus levels.
- Colour Adjustments: Adjustments in brightness, contrast, and saturation to replicate different lighting conditions.
- Adding Noise: Introducing Gaussian or salt-and-pepper noise to simulate real-world imperfections.
Advanced Image Data Augmentation Techniques:
- Neural Style Transfer: The process that involves the application of a wide range of image filters or styles, such as night vision, thermal imaging, and so on, for different conditions of visibility.
- Occlusion Simulation: Adding shadows, masks, etc., to train AI/ML models for partial visibility scenarios.
- Elastic Transformations: This technique stretches or compresses a part of the image to simulate nonrigid transformation, which is helpful in medical imaging.
Video Data
Traditional Video Data Augmentation Techniques:
- Frame Dropping: This is a method in motion recognition and surveillance whereby some frames are eliminated from video sequences to create the impression of missing frames.
- Looping Frames: Repetition of some frames to introduce variety.
- Zooming: It is the effect of zooming in or zooming out on the frames for simulating camera motions.
Advanced Video Data Augmentation Techniques:
- Scene Blending: In this technique, parts from different videos are integrated to manage transitions and generate real-world scenarios apart from the original video.
- Object Swapping: Replacing one object with another using advanced editing tools.
- Motion Blur Simulation: It adds blur effects to simulate motion, which is useful in autonomous cars and sports analysis.
- Synthetic Video Generation: This includes the generation of new videos either by GANs or even by the simulation of an environment for some rarely occurring or dangerous scenarios.
However, while these techniques offer significant benefits, it is equally important to recognise their limitations and challenges.
Limitations of Data Augmentation
While the application of data augmentation in AI/ML models offers numerous benefits in improving training dataset quality, it may not be effective in certain scenarios.
Even augmentation requires a certain volume of existing training data to be augmented. If it’s unavailable, companies may have to keep gathering data until they reach a certain level to apply these techniques effectively. Transfer learning, which involves training a model on a general dataset (like ImageNet or AugLy) and then fine-tuning it on the limited data you have available for your target application, can be an option.
Secondly, while augmentation helps diversify data, it does not directly address other challenges, such as biases or class imbalance. The augmentation process itself must be adjusted to ensure the resulting dataset does not carry the biases of the original dataset.
Best Practices for Implementing Data Augmentation
There are a few things that you need to create a robust data augmentation pipeline.
Understand the Domain and Data Type
Domain knowledge is critical for understanding the nuances of your data. It helps you identify which augmentations will preserve the integrity of the data while improving its utility for specific tasks. If you’re working with medical images, make sure that any transformations, like rotating and flipping, do not distort anatomical structures.
Second, ensure the relevance of the data. For instance, when augmenting text data in financial or legal organisations, replacing words with synonyms might change the entire meaning.
Align Augmentation with Business Needs
Your augmentations should align with the business objectives and the real-world scenarios your AI/ML models are meant to address. This ensures that the augmented data improves the AI/ML model’s ability to solve practical problems easily and improve the model’s performance in areas directly impacting your end users or clients.
Use Practical and Logical Approaches
When augmenting data, basic methods such as rotations or scaling often suffice for standard tasks like image recognition. In such cases, using complex augmentations can sometimes introduce unnecessary complications that hinder the model’s performance. Conversely, traditional methods may fall short for multimodal datasets. Choosing augmentation techniques that align with your data ensures the dataset remains effective and manageable.
Monitor Model Performance after Augmentation
Once you have augmented data, you must keep track of the models’ performance to ensure the augmentation strategy is effective. Track metrics like accuracy, precision, recall, F1-score, and AUC-ROC. Note improvements to periodically evaluate the impact of augmentation and adjust the approach wherever necessary.
Leverage Automation with Human Oversight
While automated tools efficiently process large-scale data transformations, human involvement is critical to ensure relevance and oversee complex augmentations. Find a balance between both.
Human Oversight in Data Augmentation: Why and How to Incorporate It?
Although automated augmentation makes the process scalable and efficient, it doesn’t always guarantee the accuracy or quality of labelled data. Human involvement is essential for fine-tuning augmentation techniques, ensuring datasets meet business objectives and maintaining high standards. Professionals can address gaps that automation might miss, validating and refining augmented data for better results.
However, this expertise is not always readily available in-house, and data augmentation can be a time-intensive process. As a result, many businesses opt to outsource data augmentation and labelling services. This approach ensures the final dataset is reliable, unbiased, and perfectly aligned with the intended model requirements, allowing organisations to focus on their core objectives.
Final Thoughts
While data augmentation is a great way to improve labelled dataset quality, making the process scalable and efficient, it does not guarantee the accuracy or overall quality of labelled data. Human intervention is a proven approach to ensure the process improves dataset quality. Experts can fine-tune augmentation techniques to align them with specific business goals and validate the augmented data. Organisations can manage this process internally by involving in-house experts or opt to outsource data augmentation.
Professional data collection and data labelling services help align augmented datasets with model requirements, delivering reliable results while saving time and resources.
Regardless of the approach, the goal is to get reliable, AI-ready data for more efficient model training.
