Transfer learning is a powerful technique in deep learning that allows us to take a neural network trained on one task and adapt it to a new, different dataset. Instead of training a model from scratch, we reuse knowledge already learned, saving time and computational resources.
The strategy you choose for transfer learning depends mainly on two factors: the size of the new dataset and how similar it is to the original dataset used to train the model. Based on these two factors, there are four main transfer learning scenarios.
A dataset with around one million images can be considered large, while a dataset with a few thousand images is generally considered small. The exact boundary is subjective, but overfitting becomes a major concern when working with small datasets. Dataset similarity is equally important. For example, images of dogs and wolves are considered similar because they share visual characteristics, while flower images are very different from animal images.
Each of these four cases requires a different approach.
The Demonstration Network
To explain how transfer learning works in each case, we can start from a generic pre-trained convolutional neural network. Imagine a network with three convolutional layers followed by three fully connected layers.
In general, the first convolutional layer detects simple features such as edges, the second layer detects shapes, and the deeper convolutional layers capture higher-level and more abstract features. Depending on the scenario, different parts of this network will be reused or retrained.
Small Dataset with Similar Data
When the new dataset is small and similar to the original training data, the best approach is to keep most of the pre-trained network unchanged. The final part of the network is removed and replaced with a new fully connected layer that matches the number of classes in the new dataset.
The new layer is initialized with random weights, while all the original layers are frozen and kept unchanged. Only the new layer is trained. This approach helps prevent overfitting while taking advantage of the fact that the pre-trained network already understands the relevant features of the data.
Because the datasets are similar, even the higher-level features learned by the original network remain useful.
Small Dataset with Different Data
If the dataset is small but very different from the original training data, overfitting is still a concern, but the higher-level features learned by the pre-trained model may no longer be useful.
In this case, most of the later layers of the network are removed, keeping only the early layers that capture low-level features such as edges and textures. A new fully connected layer is then added for classification. The pre-trained layers are frozen, and only the new layer is trained.
This strategy allows the model to reuse generic visual features while avoiding reliance on higher-level representations that are irrelevant to the new task.
Large Dataset with Similar Data
When the new dataset is large and similar to the original one, overfitting is much less of an issue. In this scenario, only the final classification layer is replaced to match the new number of classes, and its weights are randomly initialized.
All other layers are initialized using the pre-trained weights, and the entire network is retrained. Because the datasets are similar, the pre-trained features provide an excellent starting point, and retraining the whole network allows the model to fully adapt to the new data.
Large Dataset with Different Data
If the dataset is large and different from the original training data, there are two viable options. One approach is to use the same strategy as in the large and similar dataset case: replace the final layer, initialize the network with pre-trained weights, and retrain the entire model.
Even though the data is different, starting from pre-trained weights can speed up training and improve convergence. However, if this approach does not produce good results, the alternative is to randomly initialize all weights and train the network from scratch.
With a large dataset, both options are feasible, and experimentation will determine which works best.
Transfer learning is not a one-size-fits-all solution. The effectiveness of each strategy depends on the size and similarity of the dataset. Understanding these four cases helps you choose the right approach, reduce overfitting, and make the most of pre-trained neural networks in real-world applications.