Few-Shot Learning in Computer Vision: Practical Applications and Techniques
Keywords:
Few-shot learning, metric learning, meta-learningAbstract
Few-shot learning (FSL) generalizes models from small training sets, revolutionizing machine learning and computer vision. We discuss few-shot learning and computer vision. Few-shot learning is required when data is sparse or costly and models fail owing to inadequate training samples. This abstract covers meta-, metric-, and transfer learning. Image categorization, anomaly detection, and FSL object detection.
Using metrics, few-shot learning groups and separates similar cases. Compare and group good examples. Triplet-loss Siamese networks improve face and signature verification. Learning distance and predicting similarity may help models generalize from few instances.
Few-shot learning emphasizes meta-learning by learning to learn. This method trains models to swiftly adapt to new tasks with less data using past task experience. MAML and Prototypical Networks teach models several tasks with minimal samples. MAML rapid, low-training model parameter optimization addresses new issues.
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