Few-shot classification tends to struggle when it needs to adapt to diverse
domains. Due to the non-overlapping label space between domains, the
performance of conventional domain adaptation is limited. Previous work tackles
the problem in a transductive manner, by assuming access to the full set of
test data, which is too restrictive for many real-world applications. In this
paper, we set out to tackle this issue by introducing a inductive framework,
DaFeC, to improve Domain adaptation performance for Few-shot classification via
Clustering. We first build a representation extractor to derive features for
unlabeled data from the target domain (no test data is necessary) and then
group them with a cluster miner. The generated pseudo-labeled data and the
labeled source-domain data are used as supervision to update the parameters of
the few-shot classifier. In order to derive high-quality pseudo labels, we
propose a Clustering Promotion Mechanism, to learn better features for the
target domain via Similarity Entropy Minimization and Adversarial Distribution
Alignment, which are combined with a Cosine Annealing Strategy. Experiments are
performed on the FewRel 2.0 dataset. Our approach outperforms previous work
with absolute gains (in classification accuracy) of 4.95%, 9.55%, 3.99% and
11.62%, respectively, under four few-shot settings.

本文介绍了一种通过聚类来提高 Few-shot 分类领域适应性的归纳框架 DaFeC，其独特之处在于不需要全面访问测试数据，而是通过构建表示提取器并结合聚类矿工来得到伪标记数据，并通过聚类升级机制来实现更好的目标领域特征，从而提高分类准确性。在 FewRel 2.0 数据集上的实验表明，本文所提出的方法优于之前的研究。

基于聚类的归纳式无监督领域自适应在几类问题中的应用

Inductive Unsupervised Domain Adaptation for Few-Shot Classification via  Clustering

Low-dimensional embeddings of nodes in large graphs have proved extremely
useful in a variety of prediction tasks, from content recommendation to
identifying protein functions. However, most existing approaches require that
all nodes in the graph are present during training of the embeddings; these
previous approaches are inherently transductive and do not naturally generalize
to unseen nodes. Here we present GraphSAGE, a general, inductive framework that
leverages node feature information (e.g., text attributes) to efficiently
generate node embeddings for previously unseen data. Instead of training
individual embeddings for each node, we learn a function that generates
embeddings by sampling and aggregating features from a node's local
neighborhood. Our algorithm outperforms strong baselines on three inductive
node-classification benchmarks: we classify the category of unseen nodes in
evolving information graphs based on citation and Reddit post data, and we show
that our algorithm generalizes to completely unseen graphs using a multi-graph
dataset of protein-protein interactions.

该研究提出了一种通用的归纳框架 GraphSAGE，可以利用节点特征信息生成节点嵌入，不需要进行所有节点的嵌入训练，同时利用邻域采样和聚合特征来学习生成节点嵌入的函数。该算法在三项归纳节点分类基准测试中表现出色，具有很好的泛化性能。