“Meta-Learning: Learning to Learn Fast”, Lilian Weng2018-11-30 (RNN, meta-learning; similar)⁠:

Meta-learning, also known as “learning to learn”, intends to design models that can learn new skills or adapt to new environments rapidly with a few training examples. There are three common approaches: 1. learn an efficient distance metric (metric-based); 2. use (recurrent) network with external or internal memory (model-based); 3. optimize the model parameters explicitly for fast learning (optimization-based).

…We expect a good meta-learning model capable of well adapting or generalizing to new tasks and new environments that have never been encountered during training time. The adaptation process, essentially a mini learning session, happens during test but with a limited exposure to the new task configurations. Eventually, the adapted model can complete new tasks. This is why meta-learning is also known as learning to learn.

Define the Meta-Learning Problem · A Simple View · Training in the Same Way as Testing · Learner and Meta-Learner · Common Approaches · Metric-Based · Convolutional Siamese Neural Network · Matching Networks · Simple Embedding · Full Context Embeddings · Relation Network · Prototypical Networks · Model-Based · Memory-Augmented Neural Networks · MANN for Meta-Learning · Addressing Mechanism for Meta-Learning · Meta Networks · Fast Weights · Model Components · Training Process · Optimization-Based · LSTM Meta-Learner · Why LSTM? · Model Setup · MAML · First-Order MAML · Reptile · The Optimization Assumption · Reptile vs FOMAML · Reference

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