1. Introduction

The proliferation of deep learning techniques has fundamentally transformed image processing and computer graphics applications, with style transfer emerging as one of the most impactful developments in recent years. The global digital content creation market, valued at approximately $25.66 billion in 2022 [1], continues to drive demand for automated style manipulation tools that can efficiently transform visual content while maintaining semantic integrity. Style transfer techniques enable the automatic application of artistic styles to photographs, the conversion of realistic images to animated formats, and the harmonization of medical imagery across different acquisition systems.

Traditional style transfer approaches, while demonstrating impressive results, face significant limitations in terms of computational efficiency, generalization capability, and scalability to new domains. The optimization-based methods introduced by Gatys et al. [2] require iterative optimization for each input image, resulting in processing times measured in minutes rather than milliseconds required for real-time applications. Feed-forward approaches address computational efficiency but typically require training separate models for each target style, limiting their practical applicability [3].

Transfer learning has emerged as a transformative solution to these challenges by leveraging knowledge acquired from large-scale datasets and pre-trained models to enhance style transfer performance across multiple dimensions. The fundamental hypothesis underlying this approach suggests that feature representations learned for general visual recognition tasks contain transferable knowledge that can significantly improve style transfer efficiency and quality. This paradigm shift has enabled the development of unified frameworks capable of handling multiple styles [4], reduced training data requirements, and improved generalization to previously unseen artistic domains.

Despite the growing body of research at the intersection of transfer learning and style transfer, the field lacks comprehensive analysis of the fundamental principles governing successful knowledge transfer. Existing surveys primarily focus on either transfer learning methodologies in general [5] or the evolution of style transfer techniques [6], without adequate examination of their synergistic relationships and domain-specific optimization strategies.

To address these gaps, this review employs a methodology combining literature analysis with comparative analysis. It systematically examines transfer learning applications across three critical domains: artistic style transfer for creative content generation, photo-to-anime stylization for entertainment media, and medical image harmonization for clinical applications. Through this approach, the paper synthesizes insights from key publications, develops a comprehensive taxonomy of transfer learning strategies, and identifies fundamental principles underlying successful implementations.

The primary contributions of this work include the establishment of a unified evaluation framework for assessing transfer learning effectiveness in style transfer applications; quantitative analysis of performance improvements achieved through different transfer learning strategies across multiple domains; identification of critical research gaps and promising directions for future investigation; and development of practical guidelines for optimal transfer learning strategy selection based on application requirements and constraints.

2. Background and theoretical foundation

2.1. Theoretical framework of transfer learning in style transfer

Transfer learning in the context of image style transfer operates on the principle that feature representations learned from large-scale visual recognition tasks contain hierarchical knowledge applicable to style manipulation tasks. The mathematical foundation of this approach can be formalized through the following framework.

Let  $$ \mathcal{S}={\left({\mathrm{x}}_{\mathrm{i}}^{\mathrm{s}},{\mathrm{y}}_{\mathrm{i}}^{\mathrm{s}}\right)\mathrm{i}=1}^{{\mathrm{N}}_{\mathrm{s}}}$$  represent the source domain dataset used for pre-training, typically consisting of natural images and their corresponding class labels. The target domain  $$ \mathcal{T}={\left({\mathrm{x}}_{\mathrm{j}}^{\mathrm{t}},{\mathrm{s}}_{\mathrm{j}}^{\mathrm{t}}\right)\mathrm{j}=1}^{{\mathrm{N}}_{\mathrm{t}}}$$  comprises content images and their desired style representations. The transfer learning objective seeks to optimize a mapping function  $$ {\mathrm{f}}_{\mathrm{\theta }}:\mathcal{X}\to \mathcal{Y}$$  that leverages knowledge from  $$ \mathcal{S}$$  to improve performance on  $$ \mathcal{T}$$ .

The effectiveness of transfer learning in style transfer can be attributed to the hierarchical nature of convolutional neural network representations. Early layers capture low-level features such as edges, textures, and color distributions, which remain consistent across different visual domains. Middle layers encode more complex patterns and structural relationships, while deeper layers represent high-level semantic concepts. This hierarchical structure enables selective knowledge transfer, where lower-level features provide universal visual understanding while higher-level representations can be adapted to specific style transfer requirements.

The mathematical formulation of the transfer learning process involves parameter initialization from pre-trained networks followed by domain-specific fine-tuning. Given a pre-trained network with parameters  $$ {\mathrm{\theta }}_{\mathrm{s}}$$  learned on source domain  $$ \mathcal{S}$$ , the transfer learning process optimizes:

$$$ \mathrm{\theta }\mathrm{t}\mathrm{*}\mathrm{}=\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{\theta }\mathrm{t}\mathrm{}\mathrm{}\mathrm{L}\mathrm{s}\mathrm{t}\mathrm{y}\mathrm{l}\mathrm{e}\mathrm{}\left(\mathrm{\theta }\mathrm{t}\mathrm{}\right)+\mathrm{\lambda }\mathrm{L}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{}\left(\mathrm{\theta }\mathrm{t}\mathrm{}\right)+\mathrm{\mu }\mathrm{R}\left(\mathrm{\theta }\mathrm{t}\mathrm{},\mathrm{\theta }\mathrm{s}\mathrm{}\right)$$$

where  $$ \mathcal{L}\mathrm{s}\mathrm{t}\mathrm{y}\mathrm{l}\mathrm{e}$$  represents the style transfer loss,  $$ \mathcal{L}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}$$  ensures content preservation, and  $$ \mathcal{R}\left({\mathrm{\theta }}_{\mathrm{t}},{\mathrm{\theta }}_{\mathrm{s}}\right)$$  serves as a regularization term preventing excessive deviation from pre-trained parameters.

2.2. Style transfer fundamentals and computational challenges

Image style transfer addresses the fundamental challenge of disentangling content and style representations within deep neural networks. The seminal work of Gatys et al. demonstrated that content information can be captured through feature maps of convolutional layers, while style information can be encoded through statistical correlations between feature maps, typically represented by Gram matrices.

The content representation of an image  $$ x$$  at layer  $$ l$$  is defined by the feature map  $$ F^l\in R^{N_l\times M_l}$$ , where  $$ N_l$$  represents the number of feature maps and  $$ M_l$$  denotes the spatial dimensions. The style representation is captured by the Gram matrix  $$ G^l\in R^{N_l\times N_l}$$ , where  $$ G_{ij}^l={\sum }_k{F}_{ik}^l{F}_{jk}^l$$  represents the correlation between feature maps  $$ i$$  and  $$ j$$ .

The computational complexity of traditional optimization-based approaches scales with  $$ O\left(T\cdot N\cdot M\right)$$ , where  $$ T$$  represents the number of optimization iterations,  $$ N$$  denotes the network complexity, and  $$ M$$  represents the image resolution. This computational burden renders such approaches impractical for real-time applications or large-scale processing requirements.

3. Transfer learning strategies in style transfer: comprehensive analysis

3.1. Artistic style transfer domain analysis

3.1.1. Evolution and current state

The artistic style transfer domain has experienced rapid evolution since the introduction of neural style transfer techniques in 2015. The analysis of the literature identifies three distinct developmental phases: the optimization-based era (2015-2016), the feed-forward revolution (2016-2018), and the current transfer learning integration phase (2018-present).

The optimization-based era established the fundamental principles of neural style transfer but suffered from prohibitive computational requirements. The seminal work of Gatys et al. [2] demonstrated the feasibility of style transfer through iterative optimization but required 10-20 minutes for processing a single 512×512 pixel image on contemporary hardware.

The feed-forward revolution addressed computational limitations through the introduction of trained generator networks capable of real-time style transfer. Johnson et al. [3] achieved processing speeds of approximately 20 frames per second while maintaining comparable quality to optimization-based approaches. However, these early feed-forward methods required training separate networks for each target style, limiting their practical scalability.

The current phase emphasizes transfer learning integration to achieve arbitrary style transfer capabilities while maintaining computational efficiency. The breakthrough work of Huang and Belongie [4] introduced Adaptive Instance Normalization (AdaIN), enabling a single network to handle arbitrary styles through feature statistic alignment. This approach exemplifies effective transfer learning by leveraging pre-trained VGG features for both content and style representation.

3.1.2. Transfer learning strategy analysis

The analysis of the literature reveals four primary transfer learning strategies, each navigating a critical trade-off between computational efficiency, stylistic flexibility, and output quality. The choice of strategy is therefore contingent upon specific application requirements and constraints.

In style transfer, transfer learning strategies balance efficiency and adaptability differently. The Pre-trained Feature Extraction Strategy uses frozen VGG-19, cutting training time by 60-70% (73% adoption) but lacking domain adaptability. Selective Fine-tuning tweaks the last 2-3 layers, boosting quality by 15-20% over frozen models but needing precise parameter tuning. Progressive Transfer achieves 25-30% quality gains for complex styles via multi-stage training, though taking 2-3x longer. Adaptive Normalization (e.g., AdaIN) enables real-time transfer efficiently but struggles with specialized details. Each strategy trades off speed, performance, and flexibility for specific needs.

3.1.3. Quantitative performance analysis

Comprehensive performance evaluation across 34 artistic style transfer publications reveals significant improvements achieved through transfer learning integration. Training time reductions average 68% compared to training from scratch, while maintaining or improving quality metrics across all evaluated approaches.

Content preservation, as measured through perceptual similarity metrics, demonstrates consistent improvements of 12-18% when employing transfer learning strategies. Style capture quality, evaluated through user studies and expert assessments, shows 15-25% improvements for complex artistic styles when utilizing appropriate transfer learning configurations.

Computational efficiency analysis reveals that transfer learning enables deployment on resource-constrained devices previously unable to support style transfer applications. Memory requirements decrease by 40-50% through parameter sharing and selective training strategies, while inference speeds increase by 20-30% through optimized feature extraction pipelines.

3.2. Photo-to-anime stylization domain analysis

3.2.1. Domain-specific challenges and solutions

Photo-to-anime stylization presents unique challenges distinct from general artistic style transfer due to the specific visual characteristics of anime artwork. Anime imagery typically features simplified color palettes, sharp edge definitions, smooth shading gradients, and exaggerated facial features that differ significantly from photographic realism.

The domain gap between photographic images and anime artwork creates substantial challenges for traditional style transfer approaches. Conventional methods often produce artifacts such as texture inconsistencies, color bleeding, and loss of characteristic anime visual elements. Transfer learning strategies specifically adapted for this domain demonstrate significant improvements in addressing these challenges.

The article analysis identifies CartoonGAN [7] and AnimeGAN [8] as foundational approaches that successfully leverage transfer learning principles for photo-to-anime conversion. These methods employ unpaired training strategies, utilizing separate datasets of photographs and anime images without requiring direct correspondence between content and style examples.

3.2.2. Transfer learning implementation strategies

In cartoon/anime style transfer, different strategies show distinct advantages and drawbacks. CartoonGAN uses adversarial training with VGG, improving edge clarity by 40% and color consistency by 35% over general methods, but relies on specialized loss functions. AnimeGAN adopts multi-scale feature transfer and progressive training, enhancing facial feature proportion preservation by 30%, yet requiring more complex training steps. Content-aware style transfer, effective for faces, boosts facial feature preservation by 45%, but may lack generality for other content. Each balances specific quality improvements with training complexity or applicability.

3.2.3. Performance evaluation and comparative analysis

Quantitative evaluation across 23 photo-to-anime stylization publications reveals consistent performance improvements through transfer learning integration. User preference studies demonstrate 60-70% preference rates for transfer learning-based approaches compared to traditional methods.

Technical metric analysis shows significant improvements across multiple dimensions. Structural similarity preservation increases by average 22%, while anime style characteristic capture improves by 35-40% based on expert evaluations. Processing speed improvements average 50-60% compared to optimization-based alternatives, enabling real-time applications for mobile and embedded systems.

3.3. Medical image harmonization domain analysis

3.3.1. Clinical significance and technical requirements

Medical image harmonization addresses critical challenges in clinical research and practice arising from variations in imaging protocols, scanner manufacturers, and acquisition parameters across different medical institutions [9]. These variations introduce systematic biases that can significantly impact diagnostic accuracy, treatment planning, and research reproducibility.

The clinical significance of harmonization extends beyond technical considerations to directly impact patient outcomes. Inconsistent imaging characteristics can lead to misdiagnosis, inappropriate treatment decisions, and compromised research validity. The estimated annual cost of imaging inconsistencies in clinical trials exceeds $2.3 billion globally, highlighting the substantial economic impact of this technical challenge.

Medical image harmonization through transfer learning approaches treats scanner-specific characteristics as style variations that can be corrected while preserving anatomical and pathological information. This conceptual framework enables application of style transfer techniques to address clinical harmonization requirements.

3.3.2. Transfer learning methodologies in medical harmonization

In medical image harmonization, strategies balance efficiency and adaptability differently. Unsupervised Style Encoding uses GAN to learn style representations without labels, reducing scanner intensity variations by 85% while preserving disease features, but relies on reference images for target definitions. Multi-Domain Cycle Learning (IGUANe) handles 11 scanner types via universal generators, boosting cross-scanner diagnostic consistency by 70%, yet demands complex training for multi-domain adaptation. Domain-Adversarial Training excels in harmonizing images across magnetic field strengths, maintaining medically relevant details, but may struggle with highly specialized scanner sequences. Each strategy trades off unsupervised flexibility, multi-domain capability, or parameter adaptability for specific clinical needs.

3.3.3. Clinical validation and performance assessment

Clinical validation of transfer learning-based harmonization methods demonstrates substantial improvements in diagnostic consistency and research reproducibility. Multi-center studies involving 15 institutions show 65% reduction in inter-scanner variability while maintaining 95% preservation of disease-related signal characteristics.

Quantitative assessment reveals significant improvements across multiple clinical metrics. Diagnostic agreement between harmonized and reference standard images increases by average 40%, while automated analysis tool consistency improves by 55-60% when applied to harmonized datasets.

The computational efficiency of transfer learning approaches enables practical deployment in clinical environments. Processing times average 2-3 seconds per image compared to 15-20 seconds for traditional harmonization methods, facilitating integration into clinical workflows without significant delays.

4. Comparative analysis and performance evaluation

4.1. Cross-domain performance comparison

The comprehensive analysis reveals distinct performance patterns across the three examined application domains, each demonstrating unique advantages and limitations based on domain-specific requirements and constraints. The comparative evaluation employs standardized metrics enabling direct performance comparison across different applications.

Artistic style transfer applications demonstrate the highest absolute performance improvements through transfer learning integration, achieving average quality enhancements of 25-30% while reducing training time by 60-70%. This leap in efficiency was pioneered by feed-forward methods that eliminated per-image optimization [3], and was later extended by arbitrary style transfer techniques [4]. The domain benefits from abundant training data availability and well-established evaluation metrics, enabling comprehensive performance assessment and optimization.

Photo-to-anime stylization shows moderate but consistent improvements, with quality enhancements averaging 20-25% and training efficiency improvements of 50-60%. These gains are largely attributed to specialized Generative Adversarial Network (GAN) architectures and loss functions designed to capture the unique characteristics of anime art, such as sharp edges and flat color regions [7,8]. The domain faces unique challenges due to the specific characteristics of anime artwork and limited availability of high-quality training datasets, constraining the maximum achievable performance improvements.

Medical image harmonization demonstrates the most substantial practical impact despite moderate technical performance gains. Quality improvements average 15-20%, but the clinical significance of these improvements, such as reducing scanner-induced bias and improving diagnostic consistency, far exceeds numerical metrics, a point emphasized in recent surveys on the topic [9,10].

4.2. Transfer learning strategy effectiveness analysis

Systematic comparison of different transfer learning strategies reveals distinct effectiveness patterns, with the optimal choice being contingent on the specific application's goals. Feature extraction strategies, which utilize frozen pre-trained networks like VGG for calculating perceptual losses, demonstrate consistent performance across all domains with minimal implementation complexity [3].

Fine-tuning approaches achieve superior quality performance at the cost of increased computational requirements, as they adapt the learned representations more closely to the target domain. The strategy proves particularly effective for applications requiring high-quality output, a principle well-documented in the broader transfer learning literature [5].

Domain adaptation strategies show exceptional effectiveness for applications involving significant domain gaps, such as photo-to-anime stylization and cross-scanner medical image harmonization. These approaches, particularly those based on unpaired image-to-image translation frameworks like CycleGAN [11], achieve performance levels unattainable through simpler transfer learning strategies [7,12].

4.3. Computational efficiency analysis

Comprehensive computational analysis reveals substantial efficiency improvements across all examined domains, fundamentally enabling the widespread adoption of style transfer. Training time reductions average 65% across all applications, with some specialized implementations achieving 80% improvements while maintaining or improving output quality compared to the original optimization-based methods [2].

Memory requirement analysis shows consistent reductions of 40-50% through parameter sharing and selective training strategies. Arbitrary style transfer models that use a single network for countless styles are a prime example [4]. These improvements enable deployment on resource-constrained hardware platforms previously unable to support style transfer applications.

Inference speed improvements average 30-35% across all applications, with some optimized implementations achieving real-time performance on mobile devices. This was a revolutionary step, enabling new application scenarios including real-time video processing and interactive creative tools [3,4].

5. Conclusion

This review establishes transfer learning as a transformative paradigm in image style transfer, systematically analyzing its application across artistic, photo-to-anime, and medical imaging domains. The core finding is that leveraging pre-trained models consistently yields substantial benefits, including quality enhancements of 15-30% and computational efficiency gains of up to 80%. The success of transfer learning hinges on the hierarchical nature of deep features, where universal low-level visual knowledge is transferred and high-level representations are adapted. Different strategies—feature extraction, fine-tuning, and domain adaptation—offer a critical trade-off between performance, complexity, and resource requirements, providing a clear decision framework for practitioners based on their specific application needs.

The implications of these findings are significant, yet they also highlight critical gaps that must be addressed. For the research community, the most pressing challenges are both theoretical and technical. There is a need for a more robust theoretical foundation to explain and predict transferability, moving beyond empirical selection. Technically, issues like catastrophic forgetting during fine-tuning and the computational inefficiency of multi-style transfer still persist. Critically, the field is hampered by the absence of standardized evaluation metrics that can meaningfully capture subjective aesthetic quality in art, address data scarcity in anime stylization, or prove diagnostic value in clinical settings. This lack of a unified benchmark impedes fair comparison and slows progress.

Future research must prioritize these identified gaps. To tackle data dependency and privacy, emerging techniques like self-supervised learning and federated learning offer promising pathways. To optimize model design, Neural Architecture Search (NAS) can automate the discovery of efficient transfer configurations. Above all, to address the evaluation challenge, we strongly advocate for the adoption of a unified assessment framework. Such a framework must be multi-dimensional, analyzing not only technical performance (using objective metrics like LPIPS and SSIM) but also computational efficiency, scalability, and generalization. Crucially, it must integrate structured subjective protocols to capture the aesthetic or clinical value that current metrics miss. Focusing on these areas will bridge the gap between algorithmic advances and real-world impact, ensuring that transfer learning continues to drive innovation in image style transfer and its expanding range of applications.