Transfer Learning Explained: Overcoming Deep Learning Training Challenges

Problems with Training Deep Learning Models

1. Data hunger

   Deep learning models are extremely data-hungry. To train a reliable model, we need a large amount of data, and in most real-world cases this data must be labeled. Labeling requires manual effort, domain knowledge, and significant time, which makes it costly for organizations and companies. If the required dataset is not publicly available, collecting and annotating data adds further time, effort, and expense.

2. High training time

   Training deep learning models is computationally expensive and time-consuming. Depending on the size of the dataset and the model complexity, training can take days, weeks, or even months. This increases infrastructure costs and slows down experimentation and iteration.

Because of these two factors—high data requirements and long training time—many people and organizations prefer not to train deep learning models from scratch and instead rely on pre-trained models or transfer learning.

Using Pretrained Models

A practical solution to the training challenges in deep learning is the use of pretrained models. One of the most well-known large-scale image datasets is ImageNet, which contains around 1.4 million labeled images across more than 1,000 classes, covering everyday objects and animals such as chairs, bread, and different dog breeds. Based on this dataset, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) started around 2010. From 2012 onward, deep learning–based approaches consistently outperformed traditional methods.

Over time, several powerful architectures emerged, such as VGG and ResNet. These models are trained so well on large and diverse datasets that we can directly reuse them in our own projects. A pretrained model is simply a model that has already been trained on a huge amount of data, allowing us to leverage its learned representations instead of training everything from scratch.

However, pretrained models also come with a limitation. The data used for pretraining may not perfectly match the data required for a specific project. For example, suppose we are working on a phone vs. tablet classification problem and decide to use a VGG-based pretrained model trained on ImageNet. If ImageNet does not contain sufficient or explicit examples of phones and tablets, then the pretrained model has not learned features specifically optimized for this task. As a result, its performance may be suboptimal.

This highlights a key problem: the pretrained model was not trained on the exact type of data we want to classify. This limitation is addressed using transfer learning, where we adapt the pretrained model to our target task instead of relying on it directly.

Transfer Learning

Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. Instead of training a model from scratch, we use a pretrained model on our own dataset. This approach reduces the need for a very large dataset and significantly saves training time.

In transfer learning, the knowledge a model has learned from another dataset is reused and adapted to our target dataset. This makes problem-solving easier and more efficient, especially when data and computational resources are limited.

This idea closely mirrors how learning works in real life. For example, before learning to ride a bike, we are often advised to learn how to ride a bicycle. Riding a bike is different, but the balance and coordination learned from cycling help in the process. Similarly, if we know how to play an instrument like the sitar, it becomes easier to learn another related instrument such as the guitar because we already understand musical notes and patterns.

We apply this kind of knowledge transfer all the time in daily life—using experience from one domain to perform better in another related domain. In the machine learning and deep learning world, this concept is known as transfer learning.

How Transfer Learning Works

Let us take a simple example of cat vs dog classification using a pretrained VGG16 model. When we look at the VGG16 architecture, we can clearly see that the neural network is divided into two main parts.

The first part is the convolutional layers, often called the convolutional base. The second part consists of the fully connected layers, also known as the FC layers. The convolutional base is responsible for extracting meaningful features from the image. Since an image is essentially a 2D matrix of pixel values, the convolutional layers capture spatial relationships and local patterns such as edges, textures, and shapes. The fully connected layers use these extracted features to perform the final classification.

VGG16 is originally trained on the ImageNet dataset, which contains around 1,000 classes, making it a multi-class classification model. For this example, assume that our target classes—cat and dog—were not explicitly part of those 1,000 classes.

To apply transfer learning, we modify the pretrained model. We retain the convolutional base and remove the original fully connected layers. On top of the retained convolutional base, we add our own fully connected (dense) layers, choosing the number of layers and neurons based on our task. Finally, we add an output layer with one neuron and apply a sigmoid activation function, since this is a binary classification problem.

Next, we freeze the convolutional base, which means its weights are not updated during training. Only the newly added fully connected layers are trained on our dataset. We then train the model using whatever labeled data is available to us.

This is the core idea of transfer learning: we reuse the already learned feature representations from a large dataset like ImageNet and apply that knowledge to our specific task. This approach allows us to build effective models with less data and significantly less training time.

Why Transfer Learning Works

Consider a problem such as cat vs dog classification, where we choose a pretrained model like VGG16. The usual approach is to remove the original dense (fully connected) layers and replace them with our own dense layers. At the final stage, we use a single neuron in the output layer for binary classification. We also freeze the convolutional base, meaning its weight values are not updated during training, because these layers already contain meaningful and useful information. We then train the model using a relatively small dataset. During this process, the convolutional layers remain unchanged, and only the newly added dense layers learn from the data.

This approach works well because of how convolutional neural networks (CNNs) learn features. The convolutional layers are responsible for decoding the image and extracting features, while the later layers focus on classification. The early convolutional layers learn primitive and general features such as edges, corners, and simple shapes. As we move deeper into the network, the layers capture more complex and abstract patterns by building on these earlier features.

Models like VGG16 are trained on large datasets such as ImageNet, which contains around 1,000 classes. Objects in the real world often share common primitive features—edges, textures, and basic shapes—regardless of the specific class. Because these low-level features are universal, there is no need to relearn them for every new task. This is why we reuse the pretrained convolutional base as it is.

Instead, we only replace and retrain the dense (FC) layers so that the model can adapt to our specific classification task. The convolutional base has already learned general and transferable features during its initial training, so retraining it from scratch would be unnecessary. In simple terms, we do not reinvent the wheel—this fundamental idea is the reason transfer learning works so effectively.

Ways of Doing Transfer Learning

There are two main ways to apply transfer learning: feature extraction and fine-tuning.

The first approach is feature extraction. This is the method we discussed earlier, where we replace the last dense (fully connected) layers of the pretrained model with our own layers and freeze the entire convolutional base. During training, only the newly added dense layers are updated. Feature extraction is typically used when the target dataset is similar to the dataset on which the model was originally trained. For example, in a cat vs dog classification task, feature extraction works well because ImageNet already contains many animal images. Since the model has learned useful animal-related features, we only need to adapt the final layers to our specific classes.

The second approach is fine-tuning. In fine-tuning, we go one step further by retraining some of the deeper convolutional layers along with the newly added dense layers. Usually, the initial convolutional layers are frozen because they learn primitive features such as edges and basic shapes, which are common across most images. The later convolutional layers, however, are allowed to retrain so they can adapt to the new dataset.

Fine-tuning is applied when the target dataset is significantly different from the original training dataset. For example, in a phone vs tablet classification problem, ImageNet may not contain enough relevant examples. While the early layers can still be reused due to their generic feature representations, the deeper layers need to be retrained to learn task-specific patterns. Fine-tuning generally takes more time and computational resources, but it often leads to better performance for such datasets.