Meta-Learning with Context-Agnostic Initialisations

• URL: http://arxiv.org/abs/2007.14658v2
• Date: Thu, 22 Oct 2020 06:50:34 GMT
• Title: Meta-Learning with Context-Agnostic Initialisations
• Authors: Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmehdi, Dima Damen
• Abstract summary: We introduce a context-adversarial component into the meta-learning process. This produces an initialisation for fine-tuning to target which is context-agnostic and task-generalised. We evaluate our approach on three commonly used meta-learning algorithms and two problems.
• Score: 86.47040878540139
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Meta-learning approaches have addressed few-shot problems by finding initialisations suited for fine-tuning to target tasks. Often there are additional properties within training data (which we refer to as context), not relevant to the target task, which act as a distractor to meta-learning, particularly when the target task contains examples from a novel context not seen during training. We address this oversight by incorporating a context-adversarial component into the meta-learning process. This produces an initialisation for fine-tuning to target which is both context-agnostic and task-generalised. We evaluate our approach on three commonly used meta-learning algorithms and two problems. We demonstrate our context-agnostic meta-learning improves results in each case. First, we report on Omniglot few-shot character classification, using alphabets as context. An average improvement of 4.3% is observed across methods and tasks when classifying characters from an unseen alphabet. Second, we evaluate on a dataset for personalised energy expenditure predictions from video, using participant knowledge as context. We demonstrate that context-agnostic meta-learning decreases the average mean square error by 30%.

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