Debunking the Myth: Do Deep Learning Models Really Need Massive Data?

Thank you for sharing your perspective on the necessity of huge amounts of data for deep learning-based solutions. It is true that there are many already annotated public datasets and trained models available, and techniques like transfer learning, pseudolabeling, and synthesized datasets can help overcome the issue of limited data. Additionally, it is important to note that deep learning models do not require a large amount of memory during inference and can be updated with new data without needing to be fully retrained. Overall, it is important to consider all available options and techniques to make the most of available data and resources.