Meta-learning frameworks for few-shot learning aims to learn models that can
learn new skills or adapt to new environments rapidly with a few training
examples. This has led to the generalizability of the developed model towards
new classes with just a few labelled samples. However these networks are seen
as black-box models and understanding the representations learnt under
different learning scenarios is crucial. Neural collapse ($\mathcal{NC}$) is a
recently discovered phenomenon which showcases unique properties at the network
proceeds towards zero loss. The input features collapse to their respective
class means, the class means form a Simplex equiangular tight frame (ETF) where
the class means are maximally distant and linearly separable, and the
classifier acts as a simple nearest neighbor classifier. While these phenomena
have been observed in simple classification networks, this study is the first
to explore and understand the properties of neural collapse in meta learning
frameworks for few-shot learning. We perform studies on the Omniglot dataset in
the few-shot setting and study the neural collapse phenomenon. We observe that
the learnt features indeed have the trend of neural collapse, especially as
model size grows, but to do not necessarily showcase the complete collapse as
measured by the $\mathcal{NC}$ properties.

元学习框架用于少样本学习，旨在学习能够快速学习新技能或适应新环境的模型。本研究首次探索和理解元学习框架中神经坍塌现象的特性，观察到学习到的特征确实呈现神经坍塌趋势，然而并不完全符合神经坍塌性质的度量。通过在 Omniglot 数据集上进行研究，揭示了表示学习中的神经坍塌现象。