How Data Labeling Drives Better Generalization in Transfer Learning Models

Developers typically use the 10x rule to determine an ideal size for the training dataset to train an AI/ML model. This means that if your model has 10 features/parameters, you’d require at least 100 data points to train effectively. You can only imagine extending this rule to models with billions or trillions of parameters, such as OpenAI’s GPT-4; the world-famous AI model rumored to be trained on 1.76 trillions of parameters!

Accurate training data, especially in large volumes, is often hard to obtain. This is where transfer learning, a renowned ML technique, comes into play. It enables an AI/ML model to “generalize” learnings from one task and apply it to another. However, for generalization to work effectively, you need precisely labeled, high-quality data enriched with context and nuances to fine-tune the pre-trained model for the target task. This write-up will explore the vitality of data labeling in improving model generalization in transfer learning. 

Understanding Model Generalization in Transfer Learning

In transfer learning, model generalization is concerned with the ability of the pre-trained AI/ML model to adapt features from a source and as per a target domain. 

Key Factors That Impact Generalization in Transfer Learning

1. AI/ML Model’s Complexity

The complexity of an AI/ML model affects its ability to generalize effectively. A highly complex model can overfit by memorizing training data, making it struggle to generalize with less detailed, unseen data. On the other hand, a model that’s too simple may underfit, missing out on key patterns entirely. 

2. Chosen Regularization Technique

Regularization is a technique for controlling model complexity. It adds constraints during the training process, which discourages the model from overfitting the training data. This helps the model focus on learning only transferable patterns and not the context. Some techniques, such as L1/L2 regularization and using weight constraints, are known to improve generalization.

3. Data Splitting Strategy

The portions of splitting data into training, validation, and test segments also determine your model’s generalization ability. For example, 70% training, 20% validation, and 10% testing are often recommended for a balanced evaluation of a model’s generalizability. Typically, an 80-20 train and test split is also used as a starting point.

4. Quality (Representativeness) and Quantity of Training Data

High-quality, representative data ensures the model learns patterns that reflect real-world scenarios, reducing bias. Similarly, sufficient data volume helps the model identify consistent trends.

The Impact of Data Labeling on Model Generalization in Transfer Learning

Data labeling has emerged as a widely used approach for better model generalization in transfer learning. This can be attributed to its role in two major stages:

Improving the AI/ML training dataset for the source domain In the initial stage of transfer learning, data labeling focuses on creating a high-quality training dataset with accurate and consistent annotations. These annotations cover a range of classes and conditions, exposing the model to diverse scenarios. It helps the AI/ML model identify and learn generalizable features that are expected to be transferred to the target domain.  This step is crucial, as the AI/ML model’s learning directly influences its effectiveness in future target tasks.

Fine-tuning the pre-trained AI/ML model In the next stage, labeling data from the target domain creates a more detailed dataset for fine-tuning the pre-trained AI/ML model. This process refines the model’s understanding, enabling it to adapt effectively.  This stage is crucial, as it allows the model to apply its learning specifically to the target domain, adding significant value.

Regardless of the stage, data labeling for transfer learning is advantageous for the following reasons: 

1. Improves Quality and Consistency

Data labeling for transfer learning guarantees that both training datasets are accurately annotated and represent realistic scenarios. This provides the model with a strong foundation for learning without being affected by data gaps or inconsistencies. 

2. Provides More Context

High-quality labels for transfer learning, particularly those used for fine-tuning, add rich, contextual details to the data. For instance, when implementing image annotation for a computer vision model in an intelligent parking system, labeling a car with specific labels for the “make,” “model,” “size,” etc., instead of just passing it off as a “car” can provide more clarity to the target AI/ML model.

3. Reduces Algorithmic Bias

Biases often emerge from imbalanced training data or sampling inefficiencies, leading AI/ML models to misrepresent minority groups. Data labeling addresses this gap by ensuring underrepresented classes are accurately labeled and equally represented across all categories.

4. Facilitates Faster Model Training

Precise data labeling minimizes errors and prepares training datasets for immediate use in transfer learning. Eliminating time spent on corrections and refinements fastens the model fine-tuning process, leading to faster deployment.

5. Supports Quality Assurance

High-quality labeling ensures the model’s outputs are reliable and consistent. This is because labeling is often done under the supervision of expert annotators, especially in fields like healthcare or autonomous driving, where the consequences of sub-optimal model performance can be life-threatening.  

While there are many benefits, you must work through a few data labeling challenges. 

Challenges in Labeling Data for Transfer Learning

Data labeling is a complex process, as it forms the foundation of all AI/ML models. This makes it crucial to approach it with careful planning to address challenges such as:

1. Subjectivity and Ambiguity: Some data, like sentiment analysis text, can be interpreted differently by annotators, resulting in inconsistent labeling that affects model performance. Addressing this subjectivity requires skilled annotators with experience and domain expertise. 
2. Ensuring Transferability of Features: Labels used for the source domain must align with target domain features to ensure transferability. For example, a general sentiment classification dataset (positive, negative, neutral) might lack the granularity needed for identifying specific issues like customer dissatisfaction. To address this, labels can be refined to include markers such as “late delivery” or “poor quality,” enabling the model to handle the target task better.

3. Annotator Diversity: As data volumes increase, multiple expert annotators are often needed to manage the workload. However, this collaboration can lead to inconsistencies in interpreting labeling guidelines and may introduce personal biases. Although annotator diversity has been a known factor for years, researchers continue to explore innovative approaches to address and mitigate its impact on labeling accuracy.

4. Overfitting to Source Annotations: Overreliance on the source dataset’s specific annotations can limit adaptability. The model learns features that are too tailored to the source domain, making it less capable of adjusting to new patterns or nuances in the target domain. 
5. Finding Domain Experts: Annotating data in specialized fields like healthcare demands subject matter expertise, which can be hard to source. For example, annotators without a medical background may struggle to identify subtle discrepancies in X-rays or CT scans, compromising the reliability of annotations.
6. High Operational Overhead: Hiring domain specialists for data annotation involves substantial operational costs, including expenses for skilled personnel and required software or infrastructure. In fact, the average cost of hiring an annotation specialist starts at approximately $30 per hour and rises significantly with the complexity of the annotations required.

The Role of Human Experts in Enhancing Data Labeling for Transfer Learning

A proven approach to overcoming process-related data labeling challenges like subjectivity, overfitting, ensuring transferability, and annotator diversity is to work with expert annotators. 

Neutralizing Subjectivity

Expert annotators rely on clear guidelines and domain knowledge to create conscious and consistent labels, even when dealing with subjective data like sentiments. 

Avoiding Overfitting Source Annotations

Annotators prioritize labeling generalizable features over highly specific patterns that may not transfer well to the target domain. They do this by creating broader labels. For example, instead of elaborating on different road signs in the source dataset, they can simply create two categories, such as “cautionary” or “informational.”

Ensuring the Transferability of Features

They can carefully design annotation schemas that align with both source and target domain requirements. This is done by refining general categories/labels into more specific ones for the target domain.

Addressing Annotator Diversity Challenges

By finding a compromise between varied perspectives, annotators can overcome annotator diversity challenges in transfer learning. They are culturally aware and know how representational thinking can hamper the model’s generalization ability. 

While skilled annotators address most data labeling challenges, the process can still be labor-intensive and costly, particularly with in-house implementation (as you must have read above). Outsourcing data labeling services turns out to be a cost-effective alternative in this scenario. It gives you access to skilled labeling teams, automated workflows, and flexible engagement models like project-based or full-time engagements. They also have stringent QA processes to validate the annotated datasets, ensuring accuracy and relevance. 

End Note

As annotated data fuels AI and ML, its impact on overall model performance cannot be understated. Simply put, optimizing AI training with labeled data determines how well the model generalizes—following the classic “Garbage In, Garbage Out” principle. In transfer learning, this is even more critical, as this data serves to fine-tune the pre-trained model for the target domain. Whether you build an in-house team of skilled annotators or partner with a data annotation service provider, the goal remains the same: achieving highly accurate, precisely labeled, and AI-ready datasets to drive better model outcomes.

Read more: Data Integration Made Simple With AI/ML Technologies