The time to market for new technologies is shrinking, and companies need to be more agile and adaptable than ever before.
Traditional machine-learning models require a lot of data and computing power to train, especially when the foundation model may not be well-suited for diverse applications. This is where fine-tuning comes into play. The ultimate goal of fine-tuning is to adapt a pre-trained model to a specific new task, making it more efficient and effective for particular applications.
There are different approaches to adapting models, such as transfer learning, which leverages knowledge from a pre-trained model to improve performance on specific tasks. While we won't cover transfer learning in this article, it is worth familiarizing yourself with it as well. For now, let's take a closer look at fine-tuning in AI, how to fine-tune a pre-trained model, and its key components in the context of machine learning.
How Fine-Tuning Can Work?
A deep learning model comprises layers that extract features from input data and make predictions. Fine-tuning involves taking a pre-trained model and updating its parameters to adapt it to task-specific datasets.
This process is necessary because pre-trained models, though proficient in general tasks, may not capture the intricacies of a new dataset or task. Fine-tuning allows the model to leverage its pre-existing knowledge while refining its parameters to suit the new task's nuances better. Let’s explore an example. Let’s see an example.
Reusing pre-trained models
First, we start with a pre-trained model that has been trained on a large dataset such as ImageNet for image classification tasks, CoCo for object detection, or the well-known MNIST dataset containing handwritten text, which is often used as a starting point for testing new deep learning architectures. The pre-trained model has already been trained and is quite powerful for extracting general features from images, which can be applied to various tasks.
Training on a new dataset
Finally, we fine-tuned our model by training it on the new dataset. The neural network adjusts the weights and biases of the unfrozen layers based on the latest data. The frozen layers retain their original values, which helps preserve the general features learned from the pre-trained model.
Dropout for regularization (for deep learning networks)
Dropout for regularizing deep neural networks is a technique to prevent overfitting neural networks during training. It randomly drops out a subset of neurons and their connections at each training iteration. This randomness forces the network to learn more robust and generalizable features, leading to improved performance on unseen data.
Dropout regularization is simple yet effective, requiring minimal additional computational complexity or hyperparameter tuning.
Modifying the top layer
Next, we adjust the model architecture or modify the top layer of the pre-trained model (e.g. the output layer for image classification tasks) to match the number of classes in our new dataset. For example, suppose our original pre-trained model was trained to classify images into 1000 classes, but our new dataset only has 10 classes. In that case, we change the top layer to output 10 classes instead. This technique helps mostly with overfitting, but also among adoption to new tasks, gives better training stability. Transfer learning is often on this.
Freezing and unfreezing layers
Then, we freeze or unfreeze the parameters of all the model layers except for the top one. Freezing a layer means the weights and biases of that layer will not be updated during training. In contrast, unfreezing allows model parameters to be updated. The layers closer to the input data are usually frozen, while the top layers are unfrozen.
What’s the Difference Between Fine-Tuning, Pre-Training, and Transfer Learning?
Pre-training vs. fine tuning or fine-tuning vs. transfer learning—these terms can be confusing as they are often used interchangeably (which is not 100% accurate and precise). However, there are distinct differences between them.
Pre-training
Pre-training refers to training a model on a large dataset (e.g. mentioned ImageNet) to learn general features from the data. The pre-trained model can then be used for other tasks without further training.
Fine-tuning
Fine-tuning a model involves adapting a pre-trained model to a new task or dataset. The process consists of modifying the top layer and training on a new dataset to improve performance.
Transfer learning
Transfer learning refers to using knowledge transfer from one task or dataset to improve performance on another task or dataset. Pre-training and fine-tuning are both forms of transfer learning, but it also encompasses other techniques like domain adaptation and multi-task learning.
Fine-Tuning and Transfer Learning in the Era of Large Language Models (LLMs)
In the rapidly evolving field of artificial intelligence, large language models (LLMs) like GPT-4, BERT, and Claude 3 Opus (from Anthropic) have garnered significant attention. These models, trained on vast datasets, possess an impressive ability to understand and generate human-like text. However, to leverage their full potential for specific applications, fine-tuning and transfer learning become essential techniques.
Understanding the process
Transfer learning involves taking a pre-trained model (such as an LLM trained on diverse text corpora) and adapting it to a new, often more specific task. For instance, an LLM trained on general internet text can be fine-tuned to perform customer service automation or medical diagnosis support by exposing it to a smaller, task-specific dataset. This approach leverages the knowledge the model has already acquired, making the adaptation process more efficient and effective.
The role of fine-tuning
Fine-tuning is a crucial step that, as mentioned above, involves adjusting the parameters of the pre-trained model by continuing the training process on a new dataset relevant to the desired task. This process helps the model refine its understanding and performance in the new context. For example, an LLM pre-trained on general text can be fine-tuned with legal documents to better assist in legal research and drafting. The only limit here is usually our imagination and resources.
The Importance of Fine-Tuning in Contemporary Daily Tasks
A prime example of contemporary advancements is large language models (LLMs). Fine-tuning these models involves using algorithms to optimize and enhance the performance of a pre-trained model on a new task or dataset. Let's explore its role in detail:
Feature extraction
Feature extraction involves identifying and selecting relevant features from the input data to help the model make accurate predictions. Deep learning is done through convolutional layers that extract features from images, text, or other data types.
Model initialization
Model initialization involves setting the initial values for the model's parameters, such as weights and biases. These values can greatly impact the model's performance and are often randomly initialized before fine-tuning.
Gradient descent optimization
Gradient descent optimization is a process used to update the parameters of a model to minimize the prediction error. It calculates the gradient of the loss function and adjusts the parameters in the direction that decreases the loss.
Regularization and hyperparameter tuning
Regularization helps prevent overfitting by adding a penalty term to the loss function, encouraging the model to learn simpler patterns. Hyperparameter tuning involves finding the optimal values for parameters not learned during training, such as lower learning rate, batch size, etc.
Model evaluation and validation
To determine the effectiveness of fine-tuning, the model must be evaluated and validated on a separate dataset. The evaluation helps ensure the model is not overfitting and performs well on unseen data.
Where Fine-Tuning Can Be Used?
Natural language processing (NLP), computer vision, and speech recognition are some areas where fine-tuned models have achieved impressive results. Training a model from scratch can be computationally expensive, so fine-tuning in machine learning has become popular in many industries.
Customer service
Fine-tuned NLP models analyze customer sentiments and feedback. Fine-tuned deep learning models, like OpenAI, can also improve chatbot or virtual assistant performance in customer service.
Retail
Fine-tuned computer vision models improve the shopping experience by recommending products, classifying customer reviews, and analyzing customer behavior.
Finance
Fine-tuned models are used in the financial industry for fraud detection, risk assessment, and stock price prediction. They are trained on historical data and fine-tuned to adapt to changing market conditions.
Manufacturing
Fine-tuned models are used in manufacturing to predict equipment failures, optimize supply chain management, and improve product quality control. The models can be trained on sensor data and fine-tuned for specific production processes.
A generative AI model trained on a large dataset can be fine-tuned to generate new designs or optimize existing ones in industries such as fashion and automotive.
What are Fine-Tuning Benefits?
Fine-tuning examples show this technique offers several benefits over training a model from scratch.
Improved performance
Fine-tuning a pre-trained model can often perform better than training from scratch, especially when the training data is limited. The pre-trained model has already learned useful features that can be applied to the new task.
Faster convergence
Since the pre-trained model has already learned general features, fine-tuning can converge faster and require less training time than starting from scratch. If the pre-trained model has been trained on a similar task or dataset, it can potentially converge in just a few training epochs.
Efficient use of data
Adapting pre-trained models through fine-tuning allows for a more efficient use of data. Instead of starting from scratch and requiring a large amount of data, fine-tuning only requires a smaller dataset to adapt the pre-trained model to the new task.
Knowledge transfer
Fine-tuning pre-trained models allows for the transfer of knowledge from one task or dataset to another. The pre-trained model has already learned high-level features that can be applied to new tasks. This knowledge transfer reduces the need for large amounts of annotated data and can save time and resources.
What are Fine-Tuning Challenges?
While fine-tuning offers many benefits, some challenges must be considered when using this technique.
Overfitting
Fine-tuning a model can lead to overfitting if the training dataset is too small or the pre-trained model needs to be better suited for the new task.
For example, if you fine-tune a model trained on images of cars to classify dogs, the model may overfit and only learn features specific to vehicles.
Regularization techniques such as dropout, weight decay, or early stopping can help prevent overfitting by reducing the complexity of the model and preventing it from memorizing noise in the training data. Proper evaluation using a validation set is also essential for detecting and mitigating overfitting.
Underfitting
Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the data. This often happens when the model is not complex enough or when it's not trained for long enough. Like overfitting, underfitting can lead to poor performance on both the training and validation datasets.
For instance, if you're trying to train a model to classify images of cats and dogs but use a basic algorithm that can only distinguish between black and white pixels, it might struggle to accurately differentiate between the two animals.
To address underfitting, using more complex models or training existing models for longer periods is crucial. Additionally, increasing the size and diversity of the training dataset can help the model better capture the underlying patterns.
Legal aspects
From a legal perspective, fine-tuning raises significant concerns, particularly around data privacy and intellectual property.
Using proprietary or sensitive data for training models necessitates strict compliance with data protection regulations, such as GDPR in Europe.
Additionally, the ownership of the fine-tuned model and its outputs must be clearly defined to avoid intellectual property disputes. Ensuring transparency and accountability in how these models are fine-tuned and deployed is crucial to mitigate legal risks.
Catastrophic forgetting
When fine-tuning a model, there is a risk of catastrophic forgetting, which occurs when the model forgets previously learned information while learning new information. This can happen if the new dataset differs significantly from the pre-trained dataset.
One way to mitigate this issue is by using transfer learning, where only specific layers of the pre-trained model are fine-tuned while keeping other layers frozen. The frozen layers can retain previously learned information while the fine-tuned layers adapt to the new task.
Domain mismatch
Fine-tuning a model trained on one domain to another may not always result in optimal performance.
For example, suppose you fine-tune a model trained on news articles for sentiment analysis to analyze social media posts. In that case, the model may need to perform better due to differences in language and writing styles.
To address this, it may be necessary to fine-tune the model on a dataset from the target domain or use techniques such as data augmentation.
Computational resources
Fine-tuning a pre-trained model can be computationally expensive and require significant computational resources.
Small businesses or individuals may not access such resources, making it challenging to utilize fine-tuning techniques. However, cloud computing and services that offer pre-trained models and tools for fine-tuning can help overcome this challenge.
Partner With Us to Leverage Fine-Tuning Techniques
Fine-tuning techniques have changed the field of machine learning and made it more accessible to various industries.
Contact us to learn more about how we can help you leverage fine-tuning techniques for your company. We’ll find what can work the best for you.
