FewShotLernen

FewshotLernen, or few-shot learning, is a field in machine learning focused on enabling models to recognize or solve new tasks from only a small number of labeled examples per category. The central challenge is rapid adaptation to novel classes or tasks with limited data, often under a fixed computational budget.

Most approaches to FewshotLernen fall into three broad categories. Metric-based methods learn a similarity measure so that instances of the same class are close in a learned feature space; examples include Siamese networks and Prototypical Networks. Optimization-based methods train models to adapt quickly to new tasks, as in Model-Agnostic Meta-Learning (MAML) and its variants. Memory-augmented and hybrid approaches use external memory or structured representations to reuse knowledge from prior tasks. Episodic or episodic-like training regimes are commonly used to simulate few-shot tasks during training, improving generalization to real few-shot scenarios. Data augmentation, self-supervised pretraining, and representation learning also play important roles in improving data efficiency.

Evaluation in FewshotLernen typically uses N-way K-shot tasks, reported as mean accuracy with confidence intervals over multiple episodes. Standard benchmarks include Omniglot, MiniImageNet, CIFAR-FS, and FC100, each posing different levels of visual similarity and complexity.

Applications span computer vision, natural language processing, and robotics, where labeled data for new concepts may be scarce. Challenges remain, including robustness to distribution shift, overfitting with extremely few examples, and the need for scalable, interpretable models that can transfer across domains.