From-scratch name disambiguation is an essential task for establishing a
reliable foundation for academic platforms. It involves partitioning documents
authored by identically named individuals into groups representing distinct
real-life experts. Canonically, the process is divided into two decoupled
tasks: locally estimating the pairwise similarities between documents followed
by globally grouping these documents into appropriate clusters. However, such a
decoupled approach often inhibits optimal information exchange between these
intertwined tasks. Therefore, we present BOND, which bootstraps the local and
global informative signals to promote each other in an end-to-end regime.
Specifically, BOND harnesses local pairwise similarities to drive global
clustering, subsequently generating pseudo-clustering labels. These global
signals further refine local pairwise characterizations. The experimental
results establish BOND's superiority, outperforming other advanced baselines by
a substantial margin. Moreover, an enhanced version, BOND+, incorporating
ensemble and post-match techniques, rivals the top methods in the WhoIsWho
competition.

借助本地和全局信息交换推动全球聚类，将具有相同姓名的作者独立为不同的真实专家群体，提出了一种名为 BOND 的从零开始姓名消歧方法，通过驱动全局聚类来生成伪聚类标签，并进一步细化本地成对特征，实验证明 BOND 优于其他相对先进的基线算法，而增强版 BOND + 在 WhoIsWho 竞赛中表现不逊于其他顶级方法。

BOND: 从头开始的名称消歧与多任务推广

BOND: Bootstrapping From-Scratch Name Disambiguation with Multi-task  Promoting

Data augmentation with \textbf{Mixup} has been proven an effective method to
regularize the current deep neural networks. Mixup generates virtual samples
and corresponding labels at once through linear interpolation. However, this
one-stage generation paradigm and the use of linear interpolation have the
following two defects: (1) The label of the generated sample is directly
combined from the labels of the original sample pairs without reasonable
judgment, which makes the labels likely to be ambiguous. (2) linear combination
significantly limits the sampling space for generating samples. To tackle these
problems, we propose a novel and effective augmentation method based on global
clustering relationships named \textbf{Global Mixup}. Specifically, we
transform the previous one-stage augmentation process into two-stage,
decoupling the process of generating virtual samples from the labeling. And for
the labels of the generated samples, relabeling is performed based on
clustering by calculating the global relationships of the generated samples. In
addition, we are no longer limited to linear relationships but generate more
reliable virtual samples in a larger sampling space. Extensive experiments for
\textbf{CNN}, \textbf{LSTM}, and \textbf{BERT} on five tasks show that Global
Mixup significantly outperforms previous state-of-the-art baselines. Further
experiments also demonstrate the advantage of Global Mixup in low-resource
scenarios.

提出基于全局聚类关系的新型数据扩增方法 ——Global Mixup，其中通过将先前的一阶段扩增过程分解为两阶段，从而将生成虚拟样本的过程与标记过程分离，并根据生成样本的全局关系计算子集对生成样本的标签重新标记，从而在更大的采样空间中生成更可靠的虚拟样本。在 CNN、LSTM 和 BERT 上的五项任务的广泛实验表明，Global Mixup 明显优于现有最新技术基线。

全局混合：通过聚类消除歧义

Global Mixup: Eliminating Ambiguity with Clustering

Continual learning aims to learn new tasks incrementally using less
computation and memory resources instead of retraining the model from scratch
whenever new task arrives. However, existing approaches are designed in
supervised fashion assuming all data from new tasks have been manually
annotated, which are not practical for many real-life applications. In this
work, we propose to use pseudo label instead of the ground truth to make
continual learning feasible in unsupervised mode. The pseudo labels of new data
are obtained by applying global clustering algorithm and we propose to use the
model updated from last incremental step as the feature extractor. Due to the
scarcity of existing work, we introduce a new benchmark experimental protocol
for unsupervised continual learning of image classification task under
class-incremental setting where no class label is provided for each incremental
learning step. Our method is evaluated on the CIFAR-100 and ImageNet (ILSVRC)
datasets by incorporating the pseudo label with various existing supervised
approaches and show promising results in unsupervised scenario.

本研究提出一种用伪标签取代标准标签，在无监督模式下实现连续学习的方法，并提出了一种新的基准实验协议，以更好地评估模型在图像分类任务上的性能。结果表明，使用伪标签和现有的监督方法相结合可以在无监督场景下取得良好的成果。