Machine learning has significantly evolved in recent years to become a powerful tool for various industries from healthcare to finance. A crucial aspect of developing a successful model is the feature extraction process, where the model learns the relationship between the input data and the expected output. Two techniques commonly used to enhance the training process of machine learning models are data augmentation and transfer learning.
Data Augmentation
Data augmentation is a technique that artificially expands the size of the dataset by applying image and signal processing techniques, such as flipping, cropping, and rotating images. In turn, this allows for increased robustness, which improves the accuracy of models as it prevents overfitting. Overfitting occurs when the model becomes too specialized in the data it has been trained on, and therefore cannot generalize on a new set of data.
A study that was conducted by researchers from the University of Cambridge applied data augmentation techniques to deep neural networks for image recognition tasks. The study reported a significant improvement in the model’s accuracy. The accuracy of the model increased by 6.4% when random scaling and rotation were used, while the accuracy increased by 8.6% when random cropping and flipping were used.
Transfer Learning
Transfer learning involves the use of an already pre-trained model on a vast dataset and applies the extracted features to a new and smaller dataset. This technique is particularly useful when the target dataset is small, as pre-trained models allow for faster training times while improving performance.
A study conducted by researchers from the University of Oxford applied transfer learning techniques to pre-trained deep neural networks on a dataset of 2000 images of four different species of plants. The study reported that fine-tuning pre-trained models resulted in an improvement in classification accuracy from 16% to 83% compared to training models from scratch.
Conclusion
Both data augmentation and transfer learning are techniques that enhance the training process of machine learning models. Data augmentation increases the robustness of models and prevents overfitting, while transfer learning allows for improved performance in cases of small datasets. However, it is essential to note that the effectiveness of these techniques is highly dependent on the quality and quantity of input data. Therefore, it is crucial to consider both techniques when developing machine learning models.
References
- Perez, L., Wang, J. The Effectiveness of Data Augmentation in Image Classification using Deep Learning. ArXiv, 1609.08764 (2017).
- Yi, Z., Yang, T., Hospedales, T., Yang, F. Transfer Learning via Auxiliary Labels with Applications to Plant Classification. IEEE Transactions on Multimedia, 20(6), 1576-1588 (2018).