Synthetic Vs Real: A Comparative Study on AI Generated Art for Improved Learning in AI
Ruchi Ruchi, Lovish Kumar, Sneha Aggarwal, Tanisha Verma, Harshita, Vikas Wasson · 2024
This research offers an in-depth comparison of emotion detection models developed using real-world and synthetic datasets in the field of artificial intelligence and machine learning. The research rigorously analyses model performance, generalization capacities, and robustness in various circumstances to evaluate the use of synthetic data for machine learning applications. We provide thorough empirical research to examine essential issues concerning predictive accuracy, robustness against adversarial inputs, and biases present in synthetic datasets. Our findings indicate a distinct superiority of real-world datasets, which regularly exceed synthetic datasets in accuracy, precision, and practical utility. This result offers significant insights for researchers and practitioners, highlighting the indispensable importance of genuine data in attaining optimal performance and influencing the future of AI-based emotion recognition systems.