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 …

Training a linear classifier or lightweight model on top of pretrained vision model outputs so-
called'frozen features' leads to impressive performance on a number of downstream few …

Currently available benchmarks for few-shot learning (machine learning with few training
examples) are limited in the domains they cover, primarily focusing on image classification …

Meta-learning and other approaches to few-shot learning are widely studied for image
recognition, and are increasingly applied to other vision tasks such as pose estimation and …

Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for
ecological assessments. However, high annotation and compute costs limit the field's …

The ability for a machine learning model to cope with differences in training and deployment
conditions--eg in the presence of distribution shift or the generalization to new classes …

We introduce SubGD, a novel few-shot learning method which is based on the recent finding
that stochastic gradient descent updates tend to live in a low-dimensional parameter …

Meta-learning (ML) utilizes extracted meta-knowledge from data to enable models to
perform well on unseen data that they have not encountered before. Typically, this meta …

Few-shot classification is the ability to adapt to any new classification task from only a few
training examples. The performance of current top-performing fewshot classifiers varies …

With promising yet saturated results in high-resource settings, low-resource datasets have
gradually become crucial benchmarks (eg, BigBench Hard, superGLUE) for evaluating the …