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All Journal International Journal of Advances in Artificial Intelligence and Machine Learning
William Yeoh
Deakin Business School, Melbourne
Computer Science & IT

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Transfer Learning Effectiveness Across Domain Similarity Levels in Data Science Applications Eko Risdianto; Thai Ky Trung Pham; William Yeoh; Sultan Hammad Alshammari
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 1 (2026): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i1.656

Abstract

Background: Transfer learning has become increasingly prominent in data science due to the challenges posed by limited labeled data and distribution shifts between training and deployment environments. However, the success of transfer learning depends significantly on the structural compatibility between source and target domains.Aims: This study aims to investigate the relationship between domain similarity and transfer learning performance using an experimental framework termed Similarity-Aware Transfer Evaluation (SATE).Methods: Twelve pairs of benchmark datasets were selected to simulate varying levels of domain similarity and were made publicly available. Domain similarity was computed using Maximum Mean Discrepancy (MMD) in the learned representation space. Transfer performance was measured using a predefined Transfer Gain metric under bounded fine-tuning strategies. Correlation analysis and statistical testing were conducted to examine the relationship between similarity scores and transfer effectiveness, while fine-tuning depth was analyzed in relation to similarity magnitude.Result: The results demonstrate a strong positive correlation between domain similarity and transfer gain (r = 0.83, p < 0.01), indicating that approximately 69% of performance variability can be explained by similarity-based transfer effects. Negative transfer was observed when similarity scores were S ≤ 0.41. Furthermore, higher similarity levels were associated with deeper and more stable fine-tuning, whereas lower similarity resulted in increased instability during adaptation. These findings establish similarity as a structural compatibility constraint in transfer learning.Conclusion: The study confirms that domain similarity plays a fundamental role in determining transfer learning success. By operationalizing similarity measurement and linking it to performance thresholds, the proposed SATE framework provides a structured method for evaluating transfer feasibility in real-world data science applications.
Out of Distribution Detection for Reliable Deployment of Machine Learning Models William Yeoh; Fadly Fadly; Maria Ulfa
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 2 (2026): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i2.865

Abstract

Background: Machine learning models can give confident predictions when they receive inputs that are different from the data used during training. These predictions may not always be reliable when the model encounters unfamiliar inputs. OOD detection can help identify such inputs and reduce the risk of incorrect predictions in real-world applications.Aims: This study evaluates several OOD detection methods and examines their ability to reduce unreliable automatic predictions.Methods: Several post hoc detection methods were evaluated using ResNet classifiers trained on CIFAR datasets. The methods included Maximum Softmax Probability, ODIN, Energy-Based Detection, Mahalanobis Distance, Deep k Nearest Neighbors, and ReAct. The evaluation covered near-OOD, far-OOD, and corrupted-in-distribution conditions. Detection performance was also examined together with prediction errors, review needs, processing time, and memory use.Results: The methods showed different performance across the tested conditions. Deep k-Nearest Neighbors performed well for near OOD inputs, while ReAct showed strong performance for far OOD detection. The deployment simulation showed that ReAct-based gating reduced OOD leakage and improved the accuracy of predictions that were processed automatically. It also reduced incorrect predictions made without additional review. Energy-based detection provided competitive results with relatively low computational requirements.Conclusion: OOD detection can improve the reliability of machine learning systems. When combined with abstention and fallback mechanisms, unfamiliar inputs can be identified and sent for further review. This approach reduces unreliable predictions while allowing familiar inputs to continue through the automated process.
Co-Authors Eko Risdianto Fadly Fadly Maria Ulfa Sultan Hammad Alshammari Thai Ky Trung Pham