Deep Learning Doesn't Always Require Massive Data: Exploring Alternatives

Thank you for pointing out that there are already annotated public datasets and trained models available for deep learning-based solutions. You are correct that transfer learning, pseudolabeled, and synthesized datasets can also help overcome the issue of needing a large amount of data. Additionally, it's great to know that deep learning models don't require as much memory during inference as they do during training. It's also reassuring that the model doesn't need to be fully retrained when new data is added. Overall, I agree that the claim about the necessity of huge amounts of data for deep learning-based solutions can be misleading, and there are ways to work around this issue.