Despite deep learning's widespread success, its data-hungry and computationally
expensive nature makes it impractical for many data-constrained real-world applications …

Few-shot classification aims to adapt classifiers trained on base classes to novel classes
with a few shots. However, the limited amount of training data is often inadequate to …

The meta-learning paradigm has demonstrated significant effectiveness in few-shot text
classification. Currently, numerous efforts are grounded in metric-based learning, utilizing …

Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which states that competitive
smooth (non-binary) subnetworks exist within a dense network in continual learning tasks …

Large Language Models (LLMs) have been attracting attention due to a ability called in-
context learning (ICL). ICL, without updating the parameters of a LLM, it is possible to …

Deep learning for medical image classification has enjoyed increased attention. However, a
bottleneck that prevents it from widespread adoption is its dependency on very large …

Few-Shot Learning (FSL) refers to the problem of learning the underlying pattern in the data
just from a few training samples. However, when using transfer learning to solve few-shot …

Deep learning has achieved success in various applications, depending on the abundance
of training data. However, in practical applications, it is challenging to gather a substantial …