Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Abstract We introduce Meta-Album, an image classification meta-dataset designed to
facilitate few-shot learning, transfer learning, meta-learning, among other tasks. It includes …

Modularity is a general principle present in many fields. It offers attractive advantages,
including, among others, ease of conceptualization, interpretability, scalability, module …

Deep neural networks have demonstrated the ability to outperform humans in multiple tasks,
but they often require substantial amounts of data and computational resources. These …

Few-Shot learning is a research area of machine learning, which aims to develop a
prediction model based on a limited set of training instances. In contrast to human learners …

Many contrastive and meta-learning approaches learn representations by identifying
common features in multiple views. However, the formalism for these approaches generally …

Few-shot learning (FSL) is a central problem in meta-learning, where learners must
efficiently learn from few labeled examples. Within FSL, feature pre-training has become a …

Few-shot learning (FSL) is designed to equip models with the capability to quickly adapt to
new, unseen domains in open-world scenarios. However, there is a notable discrepancy …

We develop and study a systematic and unified methodology to organize and use scientific
challenges in research, particularly in the domain of machine learning (data-driven artificial …

We develop and study a systematic and unified methodology to organize and use scientific
challenges in research, particularly in the domain of machine learning (data-driven artificial …