The workflow for transferring learning is like that of training or pre-training. Here we’ll go over the key steps involved in a typical transfer learning workflow:
Step 1: Define the Problem and Gather Data
The first step in transfer learning is to clearly define the problem and identify the target task, ensuring that you have sufficient and suitable data for the application. If the source domain (e.g., agricultural images, sentiment analysis on online product reviews, etc.) is like the target domain (e.g., satellite images, sentiment analysis on social media posts, etc.), the pre-trained model’s learned features are more likely to transfer effectively. When domains or tasks differ significantly, such as adapting a general image recognition model to medical X-rays, additional preprocessing or intermediate steps may be needed. During this stage, data is collected, cleaned, and split into training, validation, and test sets while considering the distribution of classes and the volume of data available.
Step 2: Select a Pre-Trained Model
Choosing a pre-trained model involves evaluating its compatibility with the target task and domain. Popular models like ResNet, EfficientNet, or Vision Transformers are often used for vision-related tasks, while BERT and GPT are common for NLP tasks. For example, if you are working on sentiment analysis, a language model such as BERT pre-trained on large text corpora is a good starting point. Similarly, for object detection in traffic surveillance, a YOLO model pre-trained on COCO datasets may be ideal. When tasks and domains align closely, the selected model requires minimal adaptation; however, for tasks like depth estimation or pose detection, more advanced architectures or customizations may be needed.
Step 3: Adapt the Model for the Target Task
Adapting a pre-trained model typically involves modifying its architecture to meet the requirements of the target task. For instance, replacing the final classification head of a ResNet or other model with a fully connected layer suited to classify species of plants. This step also considers the domain’s data characteristics—for example, grayscale medical images versus RGB images of crops—to ensure compatibility between the model and the input data. Models trained for speech-to-text tasks, such as Whisper, might require fine-tuning for languages or accents not included in the original training data.
Step 4: Freeze and Unfreeze Layers
Deciding which layers to freeze or unfreeze depends on the similarity between the source and target tasks and the size of the available dataset. If the domain and task are closely aligned, such as reusing an ImageNet-trained model for a similar image classification task, freezing early layers and fine-tuning only the later ones is sufficient. For tasks with more significant domain differences, such as adapting a wildlife recognition model to underwater species, more layers should be unfrozen to allow the model to learn task-specific features. For small datasets, freezing most of the model prevents overfitting. For example, in NLP tasks like machine translation, only fine-tuning the attention layers of a pre-trained transformer model can yield excellent results while conserving computational resources.
Step 5: Train the Model
Training involves optimizing the model on the target dataset while leveraging the knowledge encoded in the pre-trained layers. For instance, when training a model to recognize defective parts in a factory setting, augmentation techniques such as rotations or flips can be used to mimic variations in real-world conditions. Smaller learning rates are often employed during fine-tuning to prevent catastrophic forgetting of the pre-trained knowledge.
Step 6: Evaluate the Model
Evaluation helps measure how well the model performs on unseen data and diagnose potential shortcomings. Metrics such as precision, recall, and F1-score are particularly useful for imbalanced datasets, as seen in fraud detection tasks. For vision models, visualizing confusion matrices or overlaying predicted bounding boxes on images can reveal systematic errors. For example, a model trained to detect cracks in pavement might fail on poorly lit images, signaling the need for additional data or preprocessing. Comparing performance against a baseline model trained from scratch can also highlight the benefits of transfer learning.
Step 7: Deploy and Fine-Tune
Once trained and evaluated, the model can be deployed in real-world settings. In production environments, it’s essential to monitor performance and retrain periodically if data shifts occur. For instance, a sentiment analysis model deployed in a social media context might require retraining to keep up with evolving language trends or slang. Post-deployment, fine-tuning with new data, such as user feedback or additional labeled examples, ensures that the model remains effective and relevant over time.
Step 8: Iterate and Optimize
The final step is to refine and optimize the model continuously. Experimenting with different pre-trained models, such as comparing ResNet with EfficientNet for image tasks, can yield insights into what works best for the given problem. For example, models trained on aerial imagery may require fine-tuning strategies like domain adaptation to account for seasonal variations in vegetation. Additionally, collecting more data, refining hyperparameters like learning rate schedules, and implementing better regularization techniques such as dropout further improve performance. Continuous evaluation and iteration are crucial to adapting the model to changing real-world conditions.
Transfer Learning Pitfalls
Let’s lay out terminology for some of common issues encountered during knowledge transfer. We’ll explore these in more depth in the last module of this course:
- Catastrophic Forgetting: The model loses previously learned knowledge when fine-tuned on a new task.
- Negative Transfer: Knowledge from the source task harms performance on the target task due to domain or task mismatch.
- *Domain Shift: Differences in data distributions between source and target domains cause poor generalization.
