Auto-Meta: Automated Gradient Based Meta Learner Search
Fully automating machine learning pipelines is one of the key challenges of current artificial intelligence research, since practical machine learning often requires costly and time-consuming human-powered processes such as model design, algorithm development, and hyperparameter tuning. In this paper, we verify that automated architecture search synergizes with the effect of gradient-based meta learning. We adopt the progressive neural architecture search \cite{liu:pnas_google:DBLP:journals/corr/abs-1712-00559} to find optimal architectures for meta-learners. The gradient based meta-learner whose architecture was automatically found achieved state-of-the-art results on the 5-shot 5-way Mini-ImageNet classification problem with $74.65\%$ accuracy, which is $11.54\%$ improvement over the result obtained by the first gradient-based meta-learner called MAML \cite{finn:maml:DBLP:conf/icml/FinnAL17}. To our best knowledge, this work is the first successful neural architecture search implementation in the context of meta learning.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningMeta-LearningNeural Architecture SearchMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
Gradient-based meta-learning methods leverage gradient descent to learn the commonalities among various tasks. While previous such methods have been successful in meta-learning tasks, they resort to simple gradient desce…
Few-Shot Image ClassificationMeta-LearningPrivacy Challenges in Meta-Learning: An Investigation on Model-Agnostic Meta-Learning
Meta-learning involves multiple learners, each dedicated to specific tasks, collaborating in a data-constrained setting. In current meta-learning methods, task learners locally learn models from sensitive data, termed su…
Meta-LearningSiamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]
Automated per-instance algorithm selection often outperforms single learners. Key to algorithm selection via meta-learning is often the (meta) features, which sometimes though do not provide enough information to train a…
Meta-LearningOnline Meta-learning for AutoML in Real-time (OnMAR)
Automated machine learning (AutoML) is a research area focusing on using optimisation techniques to design machine learning (ML) algorithms, alleviating the need for a human to perform manual algorithm design. Real-time …
AutoMLImage ClusteringMeta-LearningVideo ClassificationCooperative Meta-Learning with Gradient Augmentation
Model agnostic meta-learning (MAML) is one of the most widely used gradient-based meta-learning, consisting of two optimization loops: an inner loop and outer loop. MAML learns the new task from meta-initialization param…
Few-Shot Image Classificationimage-classificationImage ClassificationMeta-Learning+1