Prototypical Networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class. During meta-learning, it learns to learn a deep distance metric to compare a small number of images within episodes, each of which is designed to simulate the few-shot setting. The Relation Network (RN), is trained end-to-end from scratch. Matching Networks learn a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types. The two networks are the same, sharing the same weight and network parameters. There is a function above to learn the relationship between input data sample pairs. Siamese neural network is composed of two twin networks whose output is jointly trained. It should represent the relationship between inputs in the task space and facilitate problem solving. The notion of a good metric is problem-dependent. It aims to learn a metric or distance function over objects. The core idea in metric-based meta-learning is similar to nearest neighbors algorithms, which weight is generated by a kernel function. Meta Networks (MetaNet) learns a meta-level knowledge across tasks and shifts its inductive biases via fast parameterization for rapid generalization. Memory-Augmented Neural Networks Ī Memory-Augmented Neural Network, or MANN for short, is claimed to be able to encode new information quickly and thus to adapt to new tasks after only a few examples. Model-based meta-learning models updates its parameters rapidly with a few training steps, which can be achieved by its internal architecture or controlled by another meta-learner model. explicitly optimizing model parameters for fast learning (optimization-based).learning effective distance metrics (metrics-based).using (cyclic) networks with external or internal memory (model-based).Procedural bias imposes constraints on the ordering of the inductive hypotheses (e.g., preferring smaller hypotheses).Declarative bias specifies the representation of the space of hypotheses, and affects the size of the search space (e.g., represent hypotheses using linear functions only).Meta learning is concerned with two aspects of learning bias. Learning bias must be chosen dynamically.īias refers to the assumptions that influence the choice of explanatory hypotheses and not the notion of bias represented in the bias-variance dilemma.in a previous learning episode on a single dataset, or.Experience is gained by exploiting meta knowledge extracted.The system must include a learning subsystem.Definition Ī proposed definition for a meta learning system combines three requirements: In an open-ended hierarchical meta learning system using genetic programming, better evolutionary methods can be learned by meta evolution, which itself can be improved by meta meta evolution, etc. A good analogy to meta-learning, and the inspiration for Jürgen Schmidhuber's early work (1987) and Yoshua Bengio et al.'s work (1991), considers that genetic evolution learns the learning procedure encoded in genes and executed in each individual's brain. Critiques of meta learning approaches bear a strong resemblance to the critique of metaheuristic, a possibly related problem. This poses strong restrictions on the use of machine learning or data mining techniques, since the relationship between the learning problem (often some kind of database) and the effectiveness of different learning algorithms is not yet understood.īy using different kinds of metadata, like properties of the learning problem, algorithm properties (like performance measures), or patterns previously derived from the data, it is possible to learn, select, alter or combine different learning algorithms to effectively solve a given learning problem. A learning algorithm may perform very well in one domain, but not on the next. This means that it will only learn well if the bias matches the learning problem. įlexibility is important because each learning algorithm is based on a set of assumptions about the data, its inductive bias. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing learning algorithms or to learn (induce) the learning algorithm itself, hence the alternative term learning to learn. Is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments.
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