Traditional recognition methods typically require large, artificially-balanced training classes,
while few-shot learning methods are tested on artificially small ones. In contrast to both …

Many meta-learning approaches for few-shot learning rely on simple base learners such as
nearest-neighbor classifiers. However, even in the few-shot regime, discriminatively trained …

In few-shot learning scenarios, the challenge is to generalize and perform well on new
unseen examples when only very few labeled examples are available for each task. Model …

We propose a method for learning embeddings for few-shot learning that is suitable for use
with any number of shots (shot-free). Rather than fixing the class prototypes to be the …

Few-shot classification aims to learn a discriminative feature representation to recognize
unseen classes with few labeled support samples. While most few-shot learning methods …

Few-shot learning algorithms aim to learn model parameters capable of adapting to unseen
classes with the help of only a few labeled examples. A recent regularization technique …

Few-shot learning for deep neural networks is a highly challenging and key problem in
many computer vision tasks. In this context, we are targeting knowledge transfer from a set …

Few-shot learning (FSL) aims to learn a classifier that can be easily adapted to
accommodate new tasks, given only a few examples. To handle the limited-data in few-shot …

Learning with limited data is a key challenge for visual recognition. Many few-shot learning
methods address this challenge by learning an instance embedding function from seen …

The recent progress in neural architecture search (NAS) has allowed scaling the automated
design of neural architectures to real-world domains, such as object detection and semantic …