Extreme Multi-label Classification (XMC) involves predicting a subset of
relevant labels from an extremely large label space, given an input query and
labels with textual features. Models developed for this problem have
conventionally used modular approach with (i) a Dual Encoder (DE) to embed the
queries and label texts, (ii) a One-vs-All classifier to rerank the shortlisted
labels mined through meta-classifier training. While such methods have shown
empirical success, we observe two key uncharted aspects, (i) DE training
typically uses only a single positive relation even for datasets which offer
more, (ii) existing approaches fixate on using only OvA reduction of the
multi-label problem. This work aims to explore these aspects by proposing
UniDEC, a novel end-to-end trainable framework which trains the dual encoder
and classifier in together in a unified fashion using a multi-class loss. For
the choice of multi-class loss, the work proposes a novel pick-some-label (PSL)
reduction of the multi-label problem with leverages multiple (in come cases,
all) positives. The proposed framework achieves state-of-the-art results on a
single GPU, while achieving on par results with respect to multi-GPU SOTA
methods on various XML benchmark datasets, all while using 4-16x lesser compute
and being practically scalable even beyond million label scale datasets.

该研究提出了一种称为 UniDEC 的新型可端对端训练框架，该框架通过使用多类损失将双编码器和分类器一起以统一的方式进行训练，以探索极端多标签分类中的两个关键方面：(i) 双编码器训练通常仅使用单个正相关性，即使数据集提供更多；(ii) 现有方法仅集中于使用一对多的多标签问题减少。通过采用一种称为 Pick-Some-Label (PSL) 的新型多标签问题减少方法，所提出的框架在单个 GPU 上实现了最先进的结果，并在各种 XML 基准数据集上仅使用 4-16 倍的计算能力与多 GPU 最优方法相媲美，且在百万标签规模数据集上具有实际可扩展性。

UniDEC：集成双重编码器和分类器的极端多标签分类训练

UniDEC : Unified Dual Encoder and Classifier Training for Extreme  Multi-Label Classification

Universal Domain Adaptation (UNDA) aims to handle both domain-shift and
category-shift between two datasets, where the main challenge is to transfer
knowledge while rejecting unknown classes which are absent in the labeled
source data but present in the unlabeled target data. Existing methods manually
set a threshold to reject unknown samples based on validation or a pre-defined
ratio of unknown samples, but this strategy is not practical. In this paper, we
propose a method to learn the threshold using source samples and to adapt it to
the target domain. Our idea is that a minimum inter-class distance in the
source domain should be a good threshold to decide between known or unknown in
the target. To learn the inter-and intra-class distance, we propose to train a
one-vs-all classifier for each class using labeled source data. Then, we adapt
the open-set classifier to the target domain by minimizing class entropy. The
resulting framework is the simplest of all baselines of UNDA and is insensitive
to the value of a hyper-parameter yet outperforms baselines with a large
margin.

本文介绍一种使用最小类间距离阈值的通用领域适应（UNDA）方法，该方法使用源样本学习该阈值并将其适应于目标领域，同时训练一种基于一对所有分类器的学习方法。