In this paper, we address a complex but practical scenario in semi-supervised
learning (SSL) named open-set SSL, where unlabeled data contain both
in-distribution (ID) and out-of-distribution (OOD) samples. Unlike previous
methods that only consider ID samples to be useful and aim to filter out OOD
ones completely during training, we argue that the exploration and exploitation
of both ID and OOD samples can benefit SSL. To support our claim, i) we propose
a prototype-based clustering and identification algorithm that explores the
inherent similarity and difference among samples at feature level and
effectively cluster them around several predefined ID and OOD prototypes,
thereby enhancing feature learning and facilitating ID/OOD identification; ii)
we propose an importance-based sampling method that exploits the difference in
importance of each ID and OOD sample to SSL, thereby reducing the sampling bias
and improving the training. Our proposed method achieves state-of-the-art in
several challenging benchmarks, and improves upon existing SSL methods even
when ID samples are totally absent in unlabeled data.

本文探讨半监督学习中的开放集问题，提出了基于原型的聚类和识别算法以及基于重要性采样的方法，针对多项挑战性基准测试取得最优成果。

未标记数据的开放集半监督学习中的探索和利用

Exploration and Exploitation of Unlabeled Data for Open-Set  Semi-Supervised Learning

Hyperbolic manifolds for visual representation learning allow for effective
learning of semantic class hierarchies by naturally embedding tree-like
structures with low distortion within a low-dimensional representation space.
The highly separable semantic class hierarchies produced by hyperbolic learning
have shown to be powerful in low-shot tasks, however, their application in
self-supervised learning is yet to be explored fully. In this work, we explore
the use of hyperbolic representation space for self-supervised representation
learning for prototype-based clustering approaches. First, we extend the Masked
Siamese Networks to operate on the Poincar\'e ball model of hyperbolic space,
secondly, we place prototypes on the ideal boundary of the Poincar\'e ball.
Unlike previous methods we project to the hyperbolic space at the output of the
encoder network and utilise a hyperbolic projection head to ensure that the
representations used for downstream tasks remain hyperbolic. Empirically we
demonstrate the ability of these methods to perform comparatively to Euclidean
methods in lower dimensions for linear evaluation tasks, whilst showing
improvements in extreme few-shot learning tasks.

本文研究使用超几何流形来进行自监督学习中的原型聚类。作者扩展了遮蔽孪生网络以操作包含理想边界的超几何空间，并利用超几何投影来确保下游任务的表示保持超几何性。实验证明这种方法在极少样本学习任务中具有改进，并在低维线性评估任务中与欧几里得方法相当。

HMSN: 使用理想原型进行聚类的双曲超球面自监督学习

HMSN: Hyperbolic Self-Supervised Learning by Clustering with Ideal  Prototypes

Representations of events described in text are important for various tasks.
In this work, we present SWCC: a Simultaneous Weakly supervised Contrastive
learning and Clustering framework for event representation learning. SWCC
learns event representations by making better use of co-occurrence information
of events. Specifically, we introduce a weakly supervised contrastive learning
method that allows us to consider multiple positives and multiple negatives,
and a prototype-based clustering method that avoids semantically related events
being pulled apart. For model training, SWCC learns representations by
simultaneously performing weakly supervised contrastive learning and
prototype-based clustering. Experimental results show that SWCC outperforms
other baselines on Hard Similarity and Transitive Sentence Similarity tasks. In
addition, a thorough analysis of the prototype-based clustering method
demonstrates that the learned prototype vectors are able to implicitly capture
various relations between events.

SWCC 是一种同步弱监督对比学习和聚类框架，用于事件表示学习，通过利用事件的共现信息学习事件表示，同时进行弱监督对比学习和基于原型的聚类。实验结果表明，SWCC 在 Hard Similarity 和 Transitive Sentence Similarity 任务上优于其他基线。