Contrastive Learning in Image Style Transfer: A Thorough Examination using CAST and UCAST Frameworks

Computer Science & Engineering: An International Journal (CSEIJ), Vol 15, No 1, February 2025
DOI:10.5121/cseij.2025.15108 65
CONTRASTIVE LEARNING IN IMAGE STYLE
TRANSFER: A THOROUGH EXAMINATION USING
CAST AND UCAST FRAMEWORKS
ABSTRACT
In the domain of image processing and manipulation, image style transfer has emerged as a revolutionary
technique that allows the fusion of artistic styles onto photographic content. This technology has been
leveraged in recent years in fields as varied as content creation, fashion design and augmented reality
among others. Despite significant advancements, conventional style transfer methods often struggle with
preserving the content of the input image while accurately infusing the desired style. Significant
computational overheads are also often incurred during the execution of most existing style transfer
frameworks. In this paper, we examine the CAST and UCAST frameworks that rely on a contrastive
learning mechanism as a possible solution to the aforementioned challenges. This method eschews the use
of second-order statistics such as the Gram matrix for content features in favor of comparing the features
of two images side by side and extracting information based on their stylistic similarities and differences.
We provide a high-level overview of the system architecture and briefly discuss the results of an
experimental implementation of the framework.
KEYWORDS
Image Processing, Style Transfer, Contrastive Learning, Image Synthesis, Neural Networks
1. INTRODUCTION
Image style transfer is a captivating technology that has emerged at the intersection of artistry
and technology within the realm of image processing. It involves extracting high-level content
features from an input image and applying the stylistic features from a reference image onto it.
This process typically employs sophisticated algorithms, often based on deep learning models
such as convolutional neural networks (CNNs) or generative adversarial networks (GANs), to
achieve a seamless blending of content and style while preserving the semantic information of
the original image. Within the realm of image style transfer, several notable frameworks have
emerged, each offering distinct methodologies and capabilities. Prominent examples include
neural style transfer (NST) which employs deep convolutional neural networks to disentangle
content and style representations, and adaptive attention normalization (AdaAttN), which
leverages attention mechanisms and adaptive instance normalization for enhanced style transfer
fidelity.
Most existing frameworks for image style transfer utilize second-order statistics such as the
Gram matrix as proposed by Gatys et al. [2016] to produce high quality outputs. Despite the
advancements made by them in the field of arbitrary image style transfer, we contend that the use
of such feature statistics limits the ability to capture brush patterns and color distributions of
artworks precisely. As such, existing frameworks may exhibit biases towards certain artistic
styles or struggle to generalize across different datasets, leading to inconsistencies in the quality
and consistency of style transfer results.
The use of feature statistics such as mean/variance and the Gram matrix also contribute to
computational complexity and increase minimum resource requirements, posing significant
challenges, hindering the scalability and efficiency of style transfer algorithms, particularly for

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high-resolution images or real-time applications.
In this paper, we examine the contrastive learning approach as provided by the CAST and
UCAST frameworks to achieve image style transfer and potentially solve the challenges
encountered by existing techniques in the domain. This method is based on the critical idea that
it is easier to define the style features of a given image when it is compared in terms of its
similarities and differences to other artistic images.
The proposed method eschews the use of the aforementioned second-order feature statistics in
favor of extracting the style features directly from an image by employing a multi-layer projector
mechanism. Over the course of the paper, we provide an outline of the architectural model for a
contrastive learning oriented implementation and also discuss the results achieved by an
implementation created by utilizing the model’s design principles.
2. LITERATURE SURVEY
In the last decade, image style transfer has seen several advancements including the formulation
of new techniques and the optimization of existing frameworks. This section consolidates
innovations from five unique research documents, each making key contributions to the realm of
image style transfer.
[1] The first paper presents StyTr2, a novel approach to image style transfer leveraging
transformers, a popular architecture in natural language processing. The method incorporates
self-attention mechanisms to capture long-range dependencies in both content and style images,
facilitating more effective style transfer. However, StyTr2 suffers from computational
complexity, particularly when processing high-resolution images or large datasets. Transformers
typically require significant computational resources and memory overhead compared to
convolutional neural networks, which may limit the scalability of StyTr2.
[2] The second paper introduces an explicit representation for neural image style transfer,
addressing the challenge of disentangling content and style information in images. The method
utilizes a convolutional neural network architecture equipped with multiple parallel branches,
each dedicated to capturing different aspects of style. By explicitly modeling style
representations in a shared feature space, StyleBank achieves superior performance in separating
and manipulating content and style attributes.
[3] The third paper pioneers an approach to separating content and style information in the
context of artistic style transfer. The method leverages adversarial training and feature
disentanglement techniques to learn disentangled representations of content and style in an
unsupervised manner. However, its reliance on adversarial training lends to potential issues in
stability and convergence,
[4] The fourth paper proposes a method that introduces two sets of contrastive objectives that
encourage the network to learn semantically meaningful representations in both the image and
latent spaces. By simultaneously aligning the distributions of content and style features across
domains, the proposed framework facilitates more effective image translation without the need
for paired training data. Experimental results demonstrate the efficacy of Dual Contrastive
Learning for producing high-quality translations across diverse image domains, including style
transfer, colorization, and semantic segmentation.
[5] The final paper proposes an intermediate unit called a content transformation block
(CTB), specifically designed for image style transfer tasks. The CTB integrates feature-wise

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transformations into the convolutional neural network architecture, enabling the network to
disentangle content and style representations. By incorporating adaptive instance
normalization (AdaIN) and feature-wise affine transformation (FWAT) layers, the CTB
enhances the network's ability to manipulate content features while preserving the global
structure of the input image.
.
3. METHODOLOGY
This project adopts a comprehensive methodology to explore the feasibility and effectiveness
of(UCAST) for image enhancement. The methodology integrates qualitative and quantitative
research methods to provide a holistic understanding of the subject matter
Fig 3.1 Model Architecture
1. Literature Review: The initial phase involves conducting an extensive review of existing
literature, research papers, and articles related to image style transfer techniques, including
CAST and UCAST.
Explore the theoretical framework, methodologies, challenges, and applications of UCAST in
image enhancement.
2. Dataset Collection: Gather a diverse dataset of content and style images covering various
styles and content types. Ensure that the content images represent a wide range of subjects
and scenes, while the style images showcase different artistic styles and textures.
3. Preprocessing: Resize all images to a uniform size for compatibility with the style transfer
algorithm. Normalize the pixel values of the images to a consistent range, typically between 0
and 1.
4. Technical Implementation: Implement the UCAST algorithm or utilize pre-trained models
available in PyTorch.Experiment with different model architectures, hyperparameters, and
optimization techniques to achieve optimal results.
5. Style Transfer Experimentation:
Conduct style transfer experiments using the collected dataset and the implemented UCAST
algorithm. Explore the impact of different content and style image combinations on the
quality of the transferred images. Evaluate the performance of the UCAST algorithm based
on metrics such as perceptual similarity, style fidelity, and content preservation.
6. Evaluation Metrics: Evaluate the quality of the stylized images using quantitative metrics
such as Structural Similarity Index (SSI), Peak Signal-to-Noise Ratio (PSNR), and Fréchet
Inception Distance (FID). Compare the performance of the UCAST algorithm with other

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style transfer techniques to assess its superiority in image enhancement.
7. Ethical Considerations: Ethical considerations regarding data privacy, security, and user
consent will be carefully addressed throughout the research process. Measures will be
implemented to ensure the confidentiality and anonymity of participants in interviews and
data collection activities.
4. TECHNICAL FRAMEWORK
The technological framework for implementing image style transfer using CAST revolves
around leveraging deep learning architectures, neural networks, and computational models to
facilitate the transformation of images with diverse artistic styles.
This introduction describes a system for generating images using artificial intelligence. The
system takes two inputs: a style image and a content image. The style image is used as a
reference for the style of the generated image, while the content image is used as a reference
for the content of the generated image.
Once the style and content images have been provided, the system uses a generator to create
new images based on these inputs. The generator is a component of the system that has been
trained to create realistic images using a technique called adversarial loss. Adversarial loss is a
method for training machine learning models to generate data that is similar to real data.
To ensure that the generated images are realistic, the system uses two discriminators.
Discriminators are components of machine learning systems that are used to distinguish
between real and fake data. In this case, the discriminators would be used to determine
whether the images generated by the generator are realistic or not.The generator uses
adversarial loss to create realistic images, while the discriminators ensure that the generated
images are similar to real image
Fig 4.1 Activity Chart
Style Image Selection (Isc) and Content Image Selection (Ics): These are the inputs to the system.
The style image selection (Isc) is the image that the user wants to use as a reference for the style
of the generated image. The content image selection (Ics) is the image that the user wants to use
as a reference for the content of the generated image.
Generator: The generator is a component of the system that creates new images based on the
inputs it receives. In this case, the generator would use the style image selection (Isc) and content

69
image selection (Ics) to create new images.
Adversarial Loss: Adversarial loss is a technique used in machine learning to train models to
generate realistic data. In this case, the adversarial loss would be used to train the generator to
create images that are similar to real images.
Discriminators A and R: Discriminators are used in machine learning to distinguish between real
and fake data. In this case, there are two discriminators, A and R, which would be used to
determine whether the images generated by the generator are realistic or not.
5. IMPLEMENTATION
The project implementation encompasses the realization of various modules, each serving
specific functions vital to the successful operation of the CAST or UCAST application. These
modules are meticulously designed to handle distinct aspects of the image style transfer process,
ensuring a cohesive and efficient workflow from data collection to user interaction.
System Design: Define the roles and responsibilities of CAST and UCAST frameworks, such as
the style transfer algorithm, data preprocessing modules, training pipeline, and user interface.
Consider scalability, security, and interoperability requirements during the design phase to
ensure robust and efficient operation of the frameworks.
Algorithm Selection: Select appropriate algorithms and techniques for style transfer, domain
adaptation, and contrastive learning, tailored to the requirements of the CAST and UCAST
frameworks. Choose suitable deep learning frameworks and libraries, such as TensorFlow or
PyTorch, for implementing the selected algorithms.
Model Architecture Design: Here, the focus lies on conceptualizing and crafting the neural
network architecture essential for executing the style transfer process. The MSP is trained using
a contrastive learning approach and trains for 50 epochs. By determining the optimal structure,
layers, and parameters, this module enables the model to adeptly capture and transfer artistic
styles while preserving the semantics of the content.
Training Framework Development: We collect 7000 style images in different styles from Imgur
and randomly sample these images as our style dataset. We averagely sample 5000 images from
Places365 as our realistic image dataset. We train and evaluate our framework on those artistic
and realistic images. Images can be of 256 x 256 resolution to ensure uniformity and ensure no
style or content loss. The training process can take around 6-12 hours depending on the graphical
hardware components used. Assume a batch size of 32, learning rate of 0.001, and training for
100 epochs.
Integration and Deployment: Deploy the trained models to a cloud-based infrastructure, such as
AWS or Google Cloud Platform. Assume deployment on AWS EC2 instances with GPU
acceleration for real-time style transfer. The deployment process takes approximately 30 minutes
per model, and the system achieves a throughput of 100 style transfers per second.
6. RESULTS
Image style transfer is achieved by conserving the aspects of the content image and combining
it’s style aspects, to create a generated image.

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The data is placed into a datasets folder, the content images are placed into folder testA, and
style images into folder testB.
To facilitate efficient training, appropriate loss functions are employed to quantify the
disparity between the generated outputs and the target stylized images. These loss functions
encompass both content loss, which measures the divergence in content representation, and
style loss, which captures the deviation in stylistic attributes.
Throughout the training iterations, the model refines its parameters to minimize the cumulative
loss, thereby progressively improving its ability to faithfully reproduce the desired stylization
effects. This iterative optimization process continues until a satisfactory level of convergence
is achieved, signifying the completion of the training phase.
Fig 6.1 Execution Parameters
The images are tested against each other and the test.py file is executed to generate the outputs
in a results directory.
A qualitative examination of the results show that the generated images successfully preserve
the essential aspects of the content images while seamlessly incorporating the stylistic
elements from the corresponding style images.

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Fig 6.2 Generated Image Output
7. CONCLUSION
The results of the CAST and UCAST frameworks for image style transfer reveal the efficacy
and versatility of these cutting-edge technologies in transforming ordinary images into
captivating works of art. Leveraging advanced contrastive learning techniques, both
frameworks demonstrate remarkable proficiency in seamlessly integrating diverse artistic
styles onto content images while preserving their inherent semantics.
Through rigorous testing and evaluation, it becomes evident that the CAST and UCAST
frameworks excel in producing stylized outputs that exhibit remarkable perceptual fidelity and
aesthetic appeal. The generated images bear striking resemblances to the target artistic styles,
showcasing intricate brushstrokes, vibrant colors, and nuanced textures characteristic of
renowned artistic masterpieces.
Furthermore, the performance metrics obtained from extensive experimentation underscore the
superiority of these frameworks over traditional style transfer methods. With faster processing
times, lower computational resource requirements, and superior stylization quality, CAST and
UCAST emerge as formidable contenders in the realm of image style transfer.
In essence, the results of the CAST and UCAST frameworks for image style transfer reaffirm
their status as pioneering solutions in the domain of digital artistry. With their ability to
democratize artistic expression and inspire creativity across diverse user demographics, these
frameworks herald a new era of visual storytelling and aesthetic exploration.

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REFERENCES
[1] Yingying Deng, Fan Tang, Weiming Dong, Chongyang Ma, Xingjia Pan, Lei Wang, and
Changsheng Xu. 2022. StyTr2: Image Style Transfer with Transformers. In IEEE/CVF
Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Dongdong Chen, Lu Yuan, Jing Liao, Nenghai Yu, and Gang Hua. 2017. StyleBank: An explicit
representation for neural image style transfer. In IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). 1897–1906.
[3] Dmytro Kotovenko, Artsiom Sanakoyeu, Sabine Lang, and Bjorn Ommer. 2019a. Content and style
disentanglement for artistic style transfer. In IEEE/CVF International Conference on Computer
Vision (ICCV). 4422–4431.
[4] Junlin Han, Mehrdad Shoeiby, Lars Petersson, and Mohammad Ali Armin. 2021. Dual Contrastive
Learning for Unsupervised Image-to-Image Translation. In IEEE/CVF Conference on Computer
Vision and Pattern Recognition Workshops. 746–755.
[5] Dmytro Kotovenko, Artsiom Sanakoyeu, Pingchuan Ma, Sabine Lang, and Bjorn Ommer.2019b. A
Content Transformation Block for image style transfer. In IEEE/CVF

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