Comparing AI Model Training from Scratch, Transfer Learning, and Fine-tuning

Understanding the Training AI Model from Scratch Approach

Sometimes also referred to as Full Training, this is a traditional approach to training AI models. It begins with developing a new model and providing end-to-end training using large datasets to ensure it delivers the intended outcomes. The training starts by utilizing random or predefined initial weights. They are later optimized iteratively based on the input data and the output results, and by using relevant algorithms and optimization techniques.

Key components of the Training AI Model from Scratch approach include:

Data: As the model is being trained from scratch, using large datasets is essential. The model will likely suffer overfitting issues and perform poorly if trained on insufficient data.

Computational Power: This requirement is high because the model must train on all the parameters (sometimes running up to millions). It necessitates the use of very high-performance GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units)

Time Frame: Considering the high training data volume and process complexity, using this approach requires more time compared to other methods like transfer learning and fine-tuning.

Costs: The costs for training AI models using this method are high because of its extensive requirement for both human and computational resources, and a longer time frame.

Flexibility: You get complete control of the model's architecture design, which you can modify to meet specific use cases. This approach also provides full flexibility in the training process to make necessary adjustments according to available data.

Performance: The model’s performance depends on its architecture, the size of training datasets, and the level of optimization.

When to use the ‘Training from Scratch’ approach?

Even though this approach is resource, time and cost-intensive, it is best suited in the following scenarios:

Unique Issues: Use this approach if you are working on a solution to resolve a highly specialized problem for which no pre-trained model is good enough.

Custom Architecture: If the solution demands that you customize the architecture design to attain desired outcomes, go for this approach.

Availability of large datasets: As discussed earlier, this approach cannot be used if the data is not sufficient for training. So, if the required amount of data is available and your project requires complete flexibility, use this approach.

Let’s get a better understanding with a hypothetical example. Suppose, you want to build an AI solution to detect very rare cancer from medical imaging. Several pre-trained models capable of analyzing medical imaging are available. But the visual characteristics of this cancer are so unique that none of the pre-trained models are trained on the specialized features needed to identify them. This necessitates full training of a model on those particular features. Also, if the use case is such that the model architecture of existing pre-trained models is not suitable, you need to build a custom model and train it from scratch.

While using this traditional approach of ‘Training from Scratch’ is suitable in certain scenarios, it is clearly not feasible when you have resource, cost, and time constraints. Thanks to the rise of pre-trained models like Claude, YOLO, SpeechBrain, etc., you can utilize modern approaches to training AI models, like Transfer Learning and Fine-tuning, for your AI development projects.

Transfer Learning: The Approach of Using Pre-trained AI Models for Training

This AI model training approach utilizes a model pre-trained on large datasets to perform certain task/s and repurposes it to perform new yet related task/s. Suppose, you have a pre-trained Object Detection Model trained on general images like animals, plants, vehicles, etc., and you want to use it to detect food items. This model has already learned to recognize objects based on features like shapes, textures, edges, colors, etc., which are common in food items as well. Using the Transfer Learning approach, you can leverage this model to apply its ability to recognize objects in the new domain of food item detection.

So, the purpose of Transfer Learning is to utilize the knowledge learned by the model on one task to perform another task, even if the two tasks are not the same. This approach is especially useful when you have limited training data for the new task as it leverages the model's already learned features.

Let’s understand how Transfer Learning works using the above example of food item detection:

Image showing frozen and trainable layers in the Transfer Learning technique

Another method to perform Transfer Learning is Knowledge Distillation.

In this technique, the required knowledge is transferred from the pre-trained Object Detection Model (the large teacher model) to the new model (the small student model). Here knowledge includes the model’s parameters, learned patterns, and the ability to perform certain tasks (object detection). So, the student model mimics the behavior of the teacher model to perform the new task- in our example, food item detection.

The Knowledge Distillation technique is particularly useful when you need to keep the model size small while attaining the same level of performance for devices with limited resources.

Image showing knowledge transfer using the Knowledge Distillation method

When to use the ‘Transfer Learning’ approach for training AI models?

Limited Data: When you have a small training dataset for the new task and the pre-trained model is trained on a large yet relevant dataset and has features required for your new task.

Similar Tasks: Choose this approach when your new task is similar to the task your pre-trained model is proficient at performing.

Training from Scratch vs Transfer Learning

‘Transfer Learning’ saves time and costs as compared to ‘Training from Scratch’ and accomplishes the task of training AI models with limited resources. As training from scratch using a small dataset leads to overfitting issues, using ‘Transfer Learning’ significantly reduces these challenges by leveraging a model pre-trained on large datasets. The downside of ‘Transfer Learning’ is that it can be used only for similar tasks and provides limited flexibility.

Fine-tuning AI Models: A Step Beyond Transfer Learning

Fine-tuning takes a step ahead of Transfer Learning in training AI models. It also involves leveraging a pre-trained model trained on large, generic datasets. But instead of freezing the initial layers and adjusting only the final ones (as in Transfer Learning), the Fine-tuning approach allows unfreezing some of the initial layers as well and adjusting them along with the final layers to achieve performance accuracy in niche tasks.

In some cases, all the weights (parameters) of the pre-trained model are adjusted, giving more flexibility to align the model with the new task (called Full Fine-tuning). Parameter Efficient Fine-tuning (PEFT) is a modern and more feasible approach. We’ve discussed the concept of Fine-tuning in great detail in our blog titled “How LLM Fine-tuning Helps Build Custom AI Solutions for Businesses”. Please read this blog for details like examples, when to use it, and the most effective methods.

Transfer Learning vs Fine-Tuning

As the number of layers being trained in ‘Fine-tuning’ increases as compared to ‘Transfer Learning’, it utilizes moderate datasets and more computation resources. Depending on the level of Fine-tuning undertaken i.e. the number of parameters adjusted, the time and costs in this process increase. The benefit of using ‘Fine-tuning’ over ‘Transfer Learning’ is that even though the pre-trained model is chosen for similarities in the original and new tasks, it allows for a greater level of accuracy and performance optimization for new tasks. So, you get more flexibility in aligning the pre-trained model to your specialized, niche tasks.

Image showing how Transfer Learning and Fine-tuning work

Factors to Consider While Selecting Your AI Model Training Approach

We’ve discussed all three AI model training approaches, but which is right for your particular AI development project or to build your unique AI-based solution? You need to select the most suitable approach based on the availability of resources, costs, time, and the nature of your intended AI-based solution. Let’s have a quick comparison of these approaches based on your consideration factors:

Model Utilization

Data Requirements

Computational Resources

Time to Train

Flexibility in Training and Architecture

Expected Performance

Table showing the comparison between three AI model training methods

Leverage AI Development Expertise to Get High Performance Models

It’s not only about utilizing the correct technologies and tools, even the approach your AI development team adopts to train your models impacts your solution’s performance along with the cost and time needed. Even if you are building an entry-level AI application to perform one task, or a comprehensive AI-based system to automate several business processes, strategies and approaches in development are critical.

Our comprehensive AI Engineering Services focus on analyzing the client’s requirements, creating a strategy to build a custom solution, and adopting the right approaches to turn this plan into a reality. Please check out our success stories to see how we have developed AI solutions that deliver tangible results.