Project Background:
The Data Augmentation is grounded in the ever-increasing demand for high-quality, extensive, and diverse datasets to train machine learning and deep learning models effectively. In various fields such as computer vision, natural language processing, and speech recognition, the performance and generalization of models are intrinsically linked to the data they are trained on. However, collecting large and well-annotated datasets can be a daunting and costly task, limiting the potential of many machine learning applications. Data augmentation as a key strategy has emerged to address such limitations, which involves generating new training examples by applying various transformations to the existing data like image rotations, flips, cropping, or textual paraphrasing. This technique not only expands the dataset but also enhances the models ability to generalize and previously unseen examples, reducing the risk of overfitting. Moreover, data augmentation plays a major role in mitigating biases and improving the robustness of models across different demographics and real-world scenarios.
Problem Statement
- In this project, obtaining a large and diverse labeled datasets can be impractical or expensive in many domains hindering the training of effective machine learning models.
- With insufficient data, models tend to memorize training samples rather than generalize, leading to poor performance on unseen data and increased vulnerability to noise.
- Data quality and diversity are paramount for robust models, and data augmentation can address issues related to these factors.
- Developing efficient data augmentation techniques is crucial to ensure that the increased dataset size does not come at the cost of longer training times.
Aim and Objectives
- To enhance the quality and quantity of training data to improve the performance and robustness of machine learning models.
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Mitigate overfitting issues by providing models with a more diverse set of training instances to learn from.
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Improve model generalization to make accurate predictions on unseen data and diverse real-world scenarios.
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Mitigate biases and class imbalances within the training data, leading to fairer and more ethical model outcomes.
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Improve data quality by generating clean, high-fidelity examples that reflect real-world variability.
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Develop domain-specific data augmentation techniques tailored to the requirements of specific applications and fields.
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Ensure that data augmentation techniques do not significantly increase training time, maintaining computational efficiency.
Contributions to Data Augmentation
1. In this project, an augmenting training data enhances the models ability to generalize from a limited dataset leading to improved performance and accuracy on both training and test datasets.
2. Augmented data helps reduce overfitting, making models more robust in handling noise and variations present in real-world data.
3. Augmented data improves the generalization capabilities of models, allowing them to perform well even in situations not explicitly covered by the original dataset.
4. By addressing bias and fairness concerns, data augmentation plays a crucial role in promoting ethical and responsible AI.
Deep Learning Algorithms for Data Augmentation
- Generative Adversarial Networks (GANs)
- Variational Autoencoders (VAEs)
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CycleGAN
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StyleGAN
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Random Erasing
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AutoAugment
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Progressive Resizing
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Random Rotation and Flipping
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Spatial Transformer Networks (STNs)
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Feature Pyramid Networks (FPNs)
Datasets for Data Augmentation
- CIFAR-10 and CIFAR-100
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ImageNet
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MNIST
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COCO
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PASCAL VOC
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IMDB Movie Reviews
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SQuAD
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Wikipedia Text Corpora
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Medical Imaging Datasets
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Custom Image and Text Datasets
Performance Metrics
- Accuracy
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Precision
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Recall
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F1 Score
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Area Under the Receiver Operating Characteristic (ROC-AUC)
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Mean Absolute Error (MAE)
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Root Mean Square Error (RMSE)
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Cohens Kappa
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BLEU Score
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Perplexity
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Mean Average Precision
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
