Small sample learning method for external memory and meta-learning based on attention guidance

Abstract

The invention discloses a small sample learning method for external memory and meta-learning based on attention guidance, and belongs to the field of small-sample learning methods. According to the small sample learning method for external memory and meta-learning based on attention guidance, an inference network, an attention mechanism and a memory mechanism are combined for the first time; smallsample learning is carried out based on a meta-learning framework; an attention mechanism and an external memory mechanism are combined as a priori knowledge memory; a meta-learning framework and variational reasoning are combined to construct a recognizer; a priori knowledge memory combining an attention mechanism and external memory is effectively utilized; the influence caused by small samplesis compensated; moreover, by jointly learning an embedded function, mapping the representation to a potential space, storing the potential space as a value in an external memory, taking the content in the external memory as priori knowledge, and selecting similar knowledge from the external memory when a small sample arrives, the performance of the classifier under the small sample is improved.

Description

technical field [0001] The present invention relates to the field of few-shot learning methods, and more particularly, relates to a few-shot learning method based on attention-guided external memory and meta-learning. Background technique [0002] In the field of machine learning, with the emergence of more application scenarios, the problem of insufficient samples has gradually become prominent. Therefore, how to learn with small samples has become an important research direction. The current mainstream methods of learning in the case of small samples include: [0003] One is the small-sample learning method based on the meta-learning framework: through the meta-learning framework, the classifier has better generalization ability, so that it can obtain better performance under small samples; for example, Jamal M A et al. proposed a new Task-agnostic meta-learning (TAML) algorithm to avoid biased meta-learning to improve its generalization ability. [0004] The second is t...

AI Technical Summary

Problems solved by technology

[0007] In the field of machine learning, the problem of insufficient samples has gradually become prominent. At present, in machine learning, there are technologies such as attention mechanism, memory mechanism, meta-learning, and variational reasoning, but the combination of attention mechanism and external memory mechanism is the first The prior knowledge memory is combined with the meta-learning framework and variational reasoning to build a recognizer, which effectively uses the prior knowledge memory combined with the attention mechanism and external memory to make up for the impact of small samples. not seen

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