Few-shot Learning
Metric-based meta-learning techniques have successfully been applied to few-shot classification problems. In this paper, we propose to …
Deep learning has thrived by training on large-scale datasets. However, in robotics applications sample efficiency is critical. We …
Metric-based meta-learning techniques have successfully been applied to few-shot classification problems. In this paper, we propose to …
Deep learning has mainly thrived by training on large-scale datasets. However, for continual learning in applications such as robotics, …
We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and …
Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric …
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing …