Project Background:
The data augmentation using domain knowledge centers on leveraging domain-specific insights to enhance the quality and diversity of training data for machine learning models. While data augmentation techniques like rotation, flipping, and cropping are commonly employed may not fully capture the intricacies of domain-specific data. Domain knowledge encompasses expertise in the subject matter of interest and provides valuable insights into relevant transformations and perturbations that can better represent real-world variations in the data. By incorporating domain knowledge into the data augmentation process, this work ultimately generates more realistic and diverse training samples by improving the robustness and generalization of machine learning models. By harnessing domain expertise, it seeks to optimize data augmentation techniques to better align with the nuances and complexities of specific application domains, thereby enhancing the performance and reliability of machine learning models.
Problem Statement in Data Augmentation using Domain Knowledge
- Existing data augmentation techniques may fail to adequately represent the diverse and nuanced characteristics of domain-specific data, leading to suboptimal model performance.
- Standard augmentation methods such as rotation or flipping may not introduce sufficient variability to capture the full range of real-world scenarios in the data.
- Certain domains, such as medical imaging or satellite imagery, possess inherent complexities and nuances that require specialized augmentation strategies tailored to the domains unique characteristics.
- The availability of labeled data in domain-specific applications may be limited to training robust machine learning models to mitigate the effects of data scarcity by generating synthetic samples that closely resemble real-world data.
- Models trained on insufficiently augmented data may exhibit bias towards certain features or patterns in the training set, limiting their effectiveness in real-world applications.
Aim and Objectives
- Enhance the quality and diversity of training data for machine learning models through data augmentation using domain knowledge.
- Incorporate domain-specific insights to identify relevant augmentation strategies.
- Generate synthetic data samples that capture the nuances and complexities of the domain.
- Improve model robustness and generalization by diversifying the training dataset.
- Mitigate the effects of data scarcity by synthesizing additional training samples.
- Minimize overfitting and model bias by introducing realistic variations in the data.
- Optimize model performance by aligning augmentation techniques with the specific requirements of the domain.
Contributions to Data Augmentation using Domain Knowledge
- Improving the quality of training data by synthesizing samples that accurately represent real-world variations in the domain.
- Mitigating the effects of limited labeled data by generating synthetic samples supplementing the training dataset.
- Minimizing the risk of overfitting by introducing realistic variations in the data promotes better generalization to unseen examples.
- Tailoring data augmentation techniques to the specific characteristics and requirements of the domain, optimizing model performance for domain-specific tasks.
- Enhancing model interpretability by generating synthetic samples that closely resemble real-world data, facilitating a better understanding of model behavior and decision-making processes.
Deep Learning Algorithms for Data Augmentation using Domain Knowledge
- Generative Adversarial Networks (GANs)
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Variational Autoencoders (VAEs)
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CycleGAN
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StyleGAN
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Conditional GANs
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Domain Transfer Networks
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Adversarial Autoencoders
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Augmented CycleGAN
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Domain-Adversarial Neural Networks (DANNs)
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Domain-Specific Embedding Networks
Datasets for Data Augmentation using Domain Knowledge
- CIFAR-10
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CIFAR-100
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ImageNet
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MNIST
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Fashion-MNIST
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COCO
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Pascal VOC
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CelebA
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LIDC-IDRI
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ISIC
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1.Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2.Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
