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 …

Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain
disease and monitor treatment as non-invasive imaging technology. MRI produces three …

Neural Processes (NPs) are a popular class of approaches for meta-learning. Similar to
Gaussian Processes (GPs), NPs define distributions over functions and can estimate …

Neural implicit shape representations are an emerging paradigm that offers many potential
benefits over conventional discrete representations, including memory efficiency at a high …

Few-shot classification consists of a training phase where a model is learned on a relatively
large dataset and an adaptation phase where the learned model is adapted to previously …

The category gap between training and evaluation has been characterised as one of the
main obstacles to the success of Few-Shot Learning (FSL). In this paper, we for the first time …

Recently, different machine learning methods have been introduced to tackle the
challenging few-shot learning scenario that is, learning from a small labeled dataset related …

Denoising diffusion probabilistic models (DDPM) are powerful hierarchical latent variable
models with remarkable sample generation quality and training stability. These properties …

Meta-Learning aims to speed up the learning process on new tasks by acquiring useful
inductive biases from datasets of related learning tasks. While, in practice, the number of …

The success of meta-learning on out-of-distribution (OOD) tasks in the wild has proved to be
hit-and-miss. To safeguard the generalization capability of the meta-learned prior …