December 2022 Summaries

Synthetic image models are improving rapidly, particularly in text-to-image generation. These advancements offer potential applications for various enterprise problems. In this post, we discuss using synthetic image models to improve a computer vision application by addressing data drift and enhancing model accuracy under unexpected conditions. We demonstrate this through an example of autonomous farming, where a neural network is trained to distinguish crops from weeds. The classifier's performance drops when faced with changing weather patterns like snow. By efficiently generating synthetic images of plants in snowy conditions, the training dataset is augmented, and the computer vision classification accuracy improves. Synthetic data can reduce costs and speed up iteration time while maintaining high utility for downstream machine learning applications.

The article explores the use of synthetic data, generated by Gretel's ACTGAN model, in training machine learning classifiers for downstream tasks, with a focus on predicting customer purchases, such as frozen pizza. Synthetic data is highlighted as a solution to avoid linkage attacks and maintain data privacy while still enabling the training of accurate machine learning models. The data preparation involves splitting the dataset into training, test, and validation sets to evaluate the model's performance on unseen real data, ensuring no data leakage. The process includes the use of the PyCaret library to automate model selection and validation, discovering that while models trained on synthetic data may show slightly lower performance metrics compared to those trained on original data, their performance is generally comparable. This finding is significant as it implies that synthetic data can be used effectively to maintain privacy and reduce costs without compromising the effectiveness of machine learning models in real-world applications.