What is transfer learning in deep neural networks?

Transfer learning is a method in deep neural networks where knowledge acquired from training on one task is reused to improve learning on a different but related task. Instead of training a model from scratch, practitioners take a network that has learned useful internal representations on a large source dataset and adapt those representations for a target task that may have less labeled data. This approach leverages prior learning to speed up convergence, reduce the amount of required labeled data, and often improve final performance.

How transfer learning works

At the core of transfer learning is the idea of reusable representations. In convolutional networks for images, early layers often learn general features such as edges and textures while deeper layers capture more task-specific patterns. Stanford professor Fei-Fei Li and colleagues at Stanford University describe in the CS231n course materials that these learned features can be treated as a starting point for new visual tasks, either by freezing early layers and training only later layers or by fine-tuning the entire network with a smaller learning rate. From a theoretical perspective, representation learning specialists such as Yoshua Bengio at Université de Montréal argue that good intermediate representations disentangle factors of variation and therefore make transfer between tasks more feasible.

Empirical research supports this practical approach. Kaiming He at Microsoft Research and coauthors showed through experiments on deep residual networks that models pretrained on large recognition datasets provide strong initializations for a variety of downstream vision tasks, improving accuracy and stability during training. The two common strategies are feature extraction, where pretrained layers are used to generate fixed features and only a new classifier is trained, and fine-tuning, where some or all pretrained weights are adjusted on the target dataset.

Implications and risks

Transfer learning has broad relevance across scientific, commercial, and humanitarian domains. In medicine and ecology, transferring models trained on abundant data from one hospital or region to settings with limited labeled examples can accelerate diagnostic tools and species monitoring while reducing annotation costs. This can be particularly meaningful for low-income regions where labeled medical imaging data are scarce. The environmental consequence is that reuse of pretrained models often reduces the compute and energy needed compared with training large models anew, an advantage highlighted by researchers concerned with the carbon footprint of machine learning.

At the same time, transfer learning carries important risks. If the source data reflect demographic, cultural, or geographic biases, these biases can be propagated into downstream applications, producing unfair outcomes in sensitive contexts. Domain mismatch can also cause performance failures when pretrained models are applied to territories or populations not represented in the source dataset. Privacy and intellectual property concerns arise when pretrained models trained on proprietary or sensitive data are reused without appropriate safeguards.

Understanding transfer learning therefore requires technical knowledge of neural representations and practical attention to dataset provenance, cultural and territorial representativeness, and environmental and ethical trade-offs. Combining sound engineering practices with governance and domain expertise enables the benefits of transfer learning while mitigating its harms.