Yes, neural networks can work with limited data, but achieving good performance can be challenging. Neural networks typically require large amounts of labeled data to learn meaningful patterns, as they have many parameters that need optimization. However, techniques like data augmentation and transfer learning help overcome this limitation.
Data augmentation creates variations of existing data, such as flipping images or adding noise, to effectively increase the dataset size. For example, in image recognition tasks, augmentation techniques can generate diverse samples from a small dataset, improving the network's robustness. Transfer learning involves using a pre-trained model (like ResNet for images or BERT for text) and fine-tuning it on the limited dataset, leveraging knowledge gained from large-scale training.
While these techniques are effective, they do not guarantee success. For tasks with highly unique or complex data, limited data remains a challenge, and developers may need to explore hybrid approaches or collect more data to achieve desired results.