Exploring the Impact of Real and Synthetic Data in Image Classification: A Comprehensive Investigation Using CIFAKE Dataset
This research explores into the utilization of synthetic data within image classification tasks and evaluates its efficiency in comparison to the utilization of real data. To facilitate this investigation, we employ the CIFAKE dataset, comprising the well-established CIFAR10 dataset and an equivalent number of images synthetically generated using the Latent Diffusion Model (LDM). The increasing demand for diverse and abundant labeled datasets has prompted the emergence of synthetic data as a potential solution to address data scarcity. Within this study, we scrutinize the performance of image classification models trained on both real and synthetic datasets. To ensure a comprehensive evaluation, we alternately apply test data across different models. Our analysis encompasses diverse factors, including classification accuracy, generalization capabilities, and robustness in various scenarios. The findings provide valuable insights into the efficacy of synthetic data as a viable alternative or complement to real data in the realm of image classification.