Meta-Learning with Fewer Tasks through Task Interpolation. ICLR'22 作者:Huaxiu Yao, Linjun Zhang, Chelsea Finn [Transfer/ Multi-Task / Meta Learning] 这篇文章讨论了Meta-Learning问题。元学习尝试 learn to learn,将每个任务视为任务空间中的采样,通过采样出多个任务进行训练,获得在未见任务上的泛化性。
meta learning step 2 (source: https://speech.ee.ntu.edu.tw/~hylee/ml/ml2021-course-data/meta_v3.pdf) 反之,若分类器性能欠佳,则应赋予该学习算法较高的损失值L(\phi),以指导后续的优化过程。 meta learning step 2 (source: https://speech.ee.ntu.edu.tw/~hylee/ml/ml2021-course-data/meta_...
Learning many related tasks at the same time with backpropagation. Advances in neural information processing systems, pp. 657–664, 1995. [15] Giraud-Carrier, Christophe, Vilalta, Ricardo, and Brazdil, Pavel. Introduction to the special issue on meta-learning. Machine learning, 54(3):187–...
Few-shot learning is challenging for learning algorithms that learn each task in isolation and from scratch. In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is ...
To benefit the learning of a new task, meta-learning has been proposed to transfer a well-generalized meta-model learned from various meta-training tasks. Existing meta-learning algorithms randomly sample meta-training tasks with a uniform probability, under the assumption that tasks are of equal ...
即:confronting learners with (1) an open-ended series of related yet novel tasks, within which (2) preciously encountered tasks identifiably reoccur (for related observations, see Anderson, 1990; O’Donnell et al., 2009). In the present work, we formal-ize this dual learning problem, and...
tasks, a quality of meta learning. It is important to highlight that we focus on heterogeneous tasks, which are of distinct kind, in contrast to typically considered homogeneous tasks (e.g., if all tasks are classification or if all tasks are regression tasks). The fundamental idea is to ...
Continuous Meta-Learning without TasksJames HarrisonApoorva SharmaChelsea FinnMarco PavoneNeural Information Processing Systems
Crucially, pre-training configurations of model-agnostic meta-learning (MAML) that achieve high accuracies on the idealized Sen12MS target tasks are not optimal for the more realistic DFC2020 tasks with gaps up to 60% in accuracy, as shown in Experiment 1 in Table1. This performance gap is...
learning algorithm. Specifically, beyond the binary classification task of distinguishing apples from oranges (Task 1), additional binary classification tasks, such as distinguishing bicycles from cars (Task 2), are introduced, with their training datasets fed into the same learning algorithm for ...