The Foundational Role and Recent Advancements of Transfer Learning in Deep Learning
Author: Standard Technology Date: November 15, 2025
Introduction
The proliferation of deep learning (DL) models has revolutionized numerous fields, from computer vision and natural language processing to industrial automation and medical diagnostics. However, the success of these models is fundamentally dependent on the availability of vast, meticulously labeled datasets and substantial computational resources for training from scratch. This dependency presents a significant bottleneck, particularly in specialized domains where data acquisition is costly, labeling is time-consuming, or data is inherently scarce (e.g., rare medical conditions or specific industrial failure modes) [1].
Transfer Learning (TL) emerges as a critical paradigm to circumvent this limitation. At its core, transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second, related task [2]. In the context of deep learning, this involves leveraging the knowledge—specifically the learned feature representations—from a pre-trained deep neural network (DNN) on a large source domain to accelerate the training and improve the performance of a model on a smaller, target domain [3]. This approach not only dramatically reduces the need for extensive labeled data but also significantly cuts down on training time and computational expense, making advanced DL accessible to a wider range of applications.
Defining and Classifying Deep Transfer Learning
The formal definition of transfer learning involves a source domain $D_S$ and a learning task $T_S$, and a target domain $D_T$ and a learning task $T_T$. Transfer learning aims to improve the learning of the target predictive function $f_T(\cdot)$ in $D_T$ using the knowledge in $D_S$ and $T_S$, where $D_S \neq D_T$ or $T_S \neq T_T$ [4]. In deep learning, the pre-trained model's early layers, which typically learn generic features (e.g., edges, textures in images), are transferred, while the later layers, which learn task-specific features, are fine-tuned or replaced.
Deep transfer learning methods are broadly categorized based on what knowledge is transferred and how it is transferred [5]:
| Category | Description | Common Techniques | | :--- | :--- | :--- | | Instance-Based TL | Reuses data instances from the source domain by re-weighting them for the target task. | Importance sampling, instance weighting. | | Feature-Based TL | Transforms the feature representations from the source and target domains into a common, shared feature space. | Domain-invariant feature learning, autoencoders. | | Parameter-Based TL | Transfers the parameters (weights) of the pre-trained model, assuming that the source and target models share some parameters or prior distributions. | Fine-tuning, fixed feature extraction (freezing layers). | | Relational-Based TL | Transfers the relationships between data points, often used in graph-based learning. | Knowledge graph embedding, relational mapping. |
The most common and impactful method in deep learning is Parameter-Based Transfer Learning, often implemented through fine-tuning. This involves taking a model pre-trained on a massive dataset (like ImageNet for computer vision or BERT/GPT for natural language processing) and updating its weights using the target dataset. Depending on the size of the target dataset, one might freeze all layers except the final classification layer (fixed feature extraction) or fine-tune a subset of the later layers, or even the entire network [2].
Recent Advancements and Applications (2022–2025)
Recent research has focused on optimizing the transfer process and applying TL to increasingly complex and data-scarce domains. The period from 2022 to 2025 has seen significant developments, particularly in specialized fields:
1. Medical Image Analysis: Transfer learning has become the standard for medical image classification due to the limited availability of large, publicly accessible, and labeled medical datasets. A 2025 study on skin cancer classification demonstrated that combining pre-trained networks like ResNet-18 and MobileNet as feature extractors, followed by a Support Vector Machine (SVM) classifier, achieved a high accuracy of 92.87% [6]. This hybrid approach leverages the powerful feature extraction capabilities of the deep networks while using a classical machine learning classifier for the final decision boundary.
2. Industrial Anomaly Detection: In industrial settings, the dynamic nature of processes makes it impractical to acquire labeled data for every new scenario. Deep transfer learning provides a solution by transferring knowledge from general industrial data to specific anomaly detection tasks in areas like manufacturing process monitoring and predictive maintenance [1]. A comprehensive survey in 2024 highlighted the use of deep transfer learning to solve new tasks with little or no additional labeled data, bypassing the need for retraining models from scratch for every slightly different case [1].
3. Cross-Domain Transferability and Knowledge Evaluation: A key challenge is determining which knowledge is most beneficial to transfer. Recent work in 2025 has focused on understanding knowledge transferability, developing metrics to evaluate the potential of source knowledge for a given target task [7]. This research aims to move beyond heuristic choices of pre-trained models toward a more systematic and quantifiable approach to selecting the optimal source domain, thereby maximizing the performance gain from TL.
4. Few-Shot and Zero-Shot Learning: Transfer learning is the foundational principle behind few-shot and zero-shot learning, which are crucial for handling extremely limited data [8]. By transferring rich semantic and visual features from a source domain, models can generalize to new classes with only a handful of examples (few-shot) or even no examples at all (zero-shot), a capability that is essential for rapidly evolving real-world applications.
Conclusion
Transfer learning is not merely a technique but a fundamental pillar supporting the continued expansion of deep learning into real-world, data-constrained environments. By efficiently repurposing knowledge, it democratizes access to high-performance deep learning models, making them viable for specialized tasks in medicine, industry, and beyond. As research continues to refine the taxonomy of transfer methods and develop metrics for knowledge transferability, the role of deep transfer learning will only become more central to the future of artificial intelligence, driving innovation where data scarcity once posed an insurmountable barrier.
References
[1] P. Yan et al., "A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions," IEEE Access, vol. 12, pp. 3768–3789, 2024. [https://doi.org/10.1109/ACCESS.2023.3349132] [2] F. Fernandez et al., "Transfer learning assessment of small datasets relating to environmental science," Scientific Reports, vol. 15, no. 1, 2025. [https://doi.org/10.1038/s44334-025-00024-1] [3] M. Iman et al., "A Review of Deep Transfer Learning and Recent Advances," MDPI Applied Sciences, vol. 11, no. 2, 2023. [https://doi.org/10.3390/app11020040] [4] S. J. Pan and Q. Yang, "A Survey on Transfer Learning," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345–1361, 2010. (Cited for foundational taxonomy) [5] M. Hakim et al., "A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges," Ain Shams Engineering Journal, vol. 14, no. 3, 2023. [https://doi.org/10.1016/j.asej.2022.101991] [6] M. Shakya et al., "A comprehensive analysis of deep learning and transfer learning techniques for skin cancer classification," Scientific Reports, vol. 15, no. 1, 2025. [https://doi.org/10.1038/s41598-024-82241-w] [7] H. Wang et al., "Understanding Knowledge Transferability for Deep Transfer Learning," arXiv preprint arXiv:2507.03175, 2025. [8] J. Han et al., "Techniques for learning and transferring knowledge across domains," BMC Bioinformatics, vol. 26, no. 1, 2025. [https://doi.org/10.1186/s12859-025-06713-1]