Few-shot Learning
Large pre-trained models have proved to be remarkable zero- and (prompt-based) few-shot learners in unimodal vision and language tasks. …
In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance. …
Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which …
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing …
Few-shot classification is challenging because the data distribution of the training set can be widely different to the test set as …
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 …