Meta-Learning Few-Shot Learning is training systems to quickly learn new tasks from few examples, mimicking human ability to generalize from limited data through learned inductive biases — enables rapid adaptation. Meta-learning learns to learn. Few-Shot Learning Problem train on diverse tasks with few examples per task. Test on new task with few examples. Goal: learn from little data. Task Distribution different tasks sampled from task distribution. Meta-training: learn across tasks. Meta-testing: adapt to new task. Model-Agnostic Meta-Learning (MAML) gradient-based meta-learning: learn initial parameters enabling fast adaptation. Inner loop: gradient step(s) on new task. Outer loop: optimize for few-shot performance. Meta-Gradient gradient of gradient. Compute gradient for new task, then gradient of that loss at new points. Second-order derivatives. Prototypical Networks metric learning: embed examples in space, novel class centroid (prototype) is mean embedding of few examples. Classify by nearest prototype. Matching Networks attention-based: compute attention weights over support set examples, predict class via attention-weighted sum. Similar to prototypical networks. Relation Networks learn similarity metric instead of assuming Euclidean distance. Neural network predicts relation score between query and support examples. Optimization-Based Meta-Learning MAML, learned optimizers. Learn parameters enabling fast gradient descent. Metric-Based Meta-Learning prototypical networks, matching networks, relation networks. Learn embeddings/similarity. Siamese Networks pairs of inputs: same class (positive) vs. different class (negative). Contrastive loss. Learn discriminative embeddings. Memory-Augmented Networks external memory for rapid adaptation. Attention over memory stores learned knowledge. Neural Turing Machines. Embedding Learning learn good representation space where few examples suffice for classification. Representation transfer. Data Augmentation for Few-Shot augment few examples generating synthetic examples. Mixup, style transfer. Transfer Learning vs. Meta-Learning transfer: pretrain on source, finetune on target. Meta-learning: learn to finetune. Different philosophy. N-Way K-Shot N classes, K examples per class (few-shot). Standard evaluation: 5-way 5-shot. Benchmark Datasets omniglot (handwritten characters), miniImageNet, CUB (birds), Caltech-256. Cross-Domain Few-Shot train on one domain, test on another. Harder: significant distribution shift. Zero-Shot Learning no examples of new class. Use semantic attributes or word embeddings. Extreme generalization. Task Augmentation generate synthetic tasks for meta-training. Improve meta-learning. Episodic Training organize meta-training as episodes (tasks). Sample support/query sets each episode. Better matches meta-test. Uncertainty in Few-Shot Bayesian few-shot learning: posterior over parameters given few examples. Long-Tail Distribution many classes with few examples. Meta-learning naturally applicable. Domain Generalization meta-learning improves out-of-distribution generalization. Learning across diverse tasks. Multi-Task Meta-Learning meta-learn across multiple related meta-tasks. Applications robotics (quickly adapt to new environment), natural language (few-shot text classification), computer vision (few-shot object detection). Meta-Learning Frameworks learn2learn, higher libraries simplify meta-learning. Theoretical Analysis meta-learning convergence, sample complexity. Few-Shot Meta-Learning enables rapid adaptation to new tasks from minimal data, approaching human generalization.