Few-Shot vs. One-Shot vs. Zero-Shot Learning | HackerNoon
Few-shot learning (FSL) is a transformative approach in machine learning, enabling models to make accurate predictions with a limited number of examples per class. Unlike traditional methods that require extensive labeled datasets, FSL utilizes pre-trained models to adapt to new tasks through transfer learning. This process involves using a base model trained on a large dataset to fine-tune specific capabilities relevant to new classes, emphasizing the efficiency and efficacy of learning with minimal data. This is particularly advantageous in sectors like remote sensing, where the costs and effort of data gathering can be prohibitive.
Few-shot learning (FSL) is revolutionizing machine learning by enabling accurate predictions with minimal training examples, crucial for economically viable remote sensing applications.
Traditional machine learning relies on vast datasets, while FSL innovatively utilizes transfer learning to adapt pretrained models to new tasks with limited data.
In FSL, a base model, pretrained on extensive datasets, is fine-tuned using a small support set, allowing efficient adaptation to new classes.
This adaptability of FSL is particularly significant in remote sensing, where acquiring large labeled datasets can be costly or impractical.