Transfer Learning

by SACHIN ABISHEK

With the advancements in modern machine learning technologies, developing models and applications has become highly accessible and efficient. The ease and flexibility offered by contemporary tools allow models to be created with minimal or even no code, making machine learning development approachable for a broader audience across various devices.

A major breakthrough enabling this accessibility is the concept of transfer learning, a deep learning technique that allows pre-trained models to be reused for new tasks. Transfer learning works by leveraging knowledge learned from one task and applying it to another, significantly reducing the data and computational power needed for model training. This method simplifies the creation of effective models, especially for tasks involving image, audio, and gesture recognition.

Modern machine learning frameworks and platforms have embraced transfer learning to empower users to build models without specialized expertise. By using pre-trained models, users can fine-tune them with new data, drastically cutting down the time and effort required for training. This makes it possible for educators, artists, developers, and hobbyists to experiment with machine learning applications effortlessly. In a recent project, I needed to generate a model quickly with a limited dataset and resources, so I used Teachable Machine. It provided a straightforward graphical user interface (GUI) for training machine learning models directly in the browser without requiring coding expertise. Teachable Machine allowed me to create a classification model for images with ease, making machine learning more approachable for everyone.

The rapid evolution of machine learning technologies and techniques like transfer learning continues to drive innovation, making it easier for people from all backgrounds to harness the power of AI in their projects.

Transfer Learning?

Transfer Learning is a deep learning technique where a pre-trained neural network is adapted to a new, related task. Instead of training a deep neural network from scratch (which requires large amounts of data and computational resources), transfer learning leverages knowledge from a previously trained model to solve a different but related problem.

How Does Transfer Learning Work?

Transfer learning involves a structured process to leverage existing knowledge from a pre-trained model for new tasks:

1. Pre-trained Model: Start with a model already trained on a large dataset for a specific task. This pre-trained model has learned general features and patterns that are relevant across related tasks.
2. Base Model: This pre-trained model, known as the base model, includes layers that have processed data to learn hierarchical representations, capturing low-level to complex features.
3. Transfer Layers: Identify layers within the base model that hold generic information applicable to both the original and new tasks. These layers, often near the top of the network, capture broad, reusable features.
4. Fine-tuning: Fine-tune these selected layers with data from the new task. This process helps retain the pre-trained knowledge while adjusting parameters to meet the specific requirements of the new task, improving accuracy and adaptability.

Low-level features learned for task A should be beneficial for learning of model for task B.

Frozen and Trainable Layers

In Transfer Learning, two main components help in adapting models effectively: frozen layers and modifiable layers.

1. Frozen Layers: These layers from a pre-trained model remain unchanged during fine-tuning. They retain general features learned from the original task, extracting universal patterns from input data.
2. Modifiable Layers: These layers are adjusted during fine-tuning to learn task-specific features from the new dataset, allowing the model to meet the new task’s unique requirements.

Applications of Transfer Learning

Transfer learning is widely used across multiple domains, including:

1. Computer Vision: Transfer learning is prevalent in image recognition tasks, where models pre-trained on large image datasets are adapted to specific tasks such as medical imaging, facial recognition, and object detection.
2. Natural Language Processing (NLP): In NLP, models like BERT, GPT, and ELMo are pre-trained on vast text corpora and later fine-tuned for specific tasks such as sentiment analysis, machine translation, and question-answering.
3. Healthcare: Transfer learning helps develop medical diagnostic tools, leveraging knowledge from general image recognition models to analyze medical images like X-rays or MRIs.
4. Finance: Transfer learning in finance assists in fraud detection, risk assessment, and credit scoring by transferring patterns learned from related financial datasets.

Advantages of Transfer Learning

• Speed up the training process: By using a pre-trained model, the model can learn more quickly and effectively on the second task, as it already has a good understanding of the features and patterns in the data.
• Better performance: Transfer learning can lead to better performance on the second task, as the model can leverage the knowledge it has gained from the first task.
• Handling small datasets: When there is limited data available for the second task, transfer learning can help to prevent overfitting, as the model will have already learned general features that are likely to be useful in the second task.

Disadvantages of Transfer Learning

• Domain mismatch: The pre-trained model may not be well-suited to the second task if the two tasks are vastly different or the data distribution between the two tasks is very different.
• Overfitting: Transfer learning can lead to overfitting if the model is fine-tuned too much on the second task, as it may learn task-specific features that do not generalize well to new data.
• Complexity: The pre-trained model and the fine-tuning process can be computationally expensive and may require specialized hardware.