Optimization as a Model for Few-Shot Learning
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Abstract
Though deep neural networks have shown great success in the large data domain, they generally perform poorly on few-shot learning tasks, where a model has to quickly generalize after seeing very few examples from each class. The general belief is that gradient-based optimization in high capacity models requires many iterative steps over many examples to perform well. Here, we propose an LSTM-based meta-learner model to learn the exact optimization algorithm used to train another learner neural network in the few-shot regime. The parametrization of our model allows it to learn appropriate parameter updates specifically for the scenario where a set amount of updates will be made, while also learning a general initialization of the learner network that allows for quick convergence of training. We demonstrate that this meta-learning model is competitive with deep metric-learning techniques for few-shot learning.
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The record
- Venue
- International Conference on Learning Representations
- Topic
- Domain Adaptation and Few-Shot Learning
- Field
- Computer Science
- Canadian institutions
- Université de Sherbrooke
- Funders
- —
- Keywords
- Computer scienceInitializationArtificial intelligenceMeta learning (computer science)Convergence (economics)Deep learningMetric (unit)Artificial neural networkMachine learningSet (abstract data type)Parametrization (atmospheric modeling)Competitive learningClass (philosophy)Domain (mathematical analysis)Learning to learnMathematicsTask (project management)
- Has abstract in OpenAlex
- yes