Deep neural networks (DNNs) often perform poorly in the presence of domain
shift and category shift. How to upcycle DNNs and adapt them to the target task
remains an important open problem. Unsupervised Domain Adaptation (UDA),
especially recently proposed Source-free Domain Adaptation (SFDA), has become a
promising technology to address this issue. Nevertheless, existing SFDA methods
require that the source domain and target domain share the same label space,
consequently being only applicable to the vanilla closed-set setting. In this
paper, we take one step further and explore the Source-free Universal Domain
Adaptation (SF-UniDA). The goal is to identify "known" data samples under both
domain and category shift, and reject those "unknown" data samples (not present
in source classes), with only the knowledge from standard pre-trained source
model. To this end, we introduce an innovative global and local clustering
learning technique (GLC). Specifically, we design a novel, adaptive one-vs-all
global clustering algorithm to achieve the distinction across different target
classes and introduce a local k-NN clustering strategy to alleviate negative
transfer. We examine the superiority of our GLC on multiple benchmarks with
different category shift scenarios, including partial-set, open-set, and
open-partial-set DA. Remarkably, in the most challenging open-partial-set DA
scenario, GLC outperforms UMAD by 14.8\% on the VisDA benchmark. The code is
available at this https URL.

本文提出了一种基于全局和本地聚类的学习技术（GLC），以实现知道领域和类别转换下已知数据样本的识别，并仅使用标准预训练源模型的知识从而拒绝那些 “未知” 的数据样本。我们检验了我们的方法在包括部分集、开放集和开放部分集 DA 等不同类别转化情境下的多个基准测试中相对于 UMAD 的优越性，值得注意的是，我们的方法在最具挑战性的开放部分集 DA 情境下比 UMAD 高出 14.8％。

基于领域和类别转移的升级模型

Upcycling Models under Domain and Category Shift

Domain adaptation for semantic segmentation across datasets consisting of the
same categories has seen several recent successes. However, a more general
scenario is when the source and target datasets correspond to non-overlapping
label spaces. For example, categories in segmentation datasets change vastly
depending on the type of environment or application, yet share many valuable
semantic relations. Existing approaches based on feature alignment or
discrepancy minimization do not take such category shift into account. In this
work, we present Cluster-to-Adapt (C2A), a computationally efficient
clustering-based approach for domain adaptation across segmentation datasets
with completely different, but possibly related categories. We show that such a
clustering objective enforced in a transformed feature space serves to
automatically select categories across source and target domains that can be
aligned for improving the target performance, while preventing negative
transfer for unrelated categories. We demonstrate the effectiveness of our
approach through experiments on the challenging problem of outdoor to indoor
adaptation for semantic segmentation in few-shot as well as zero-shot settings,
with consistent improvements in performance over existing approaches and
baselines in all cases.

本研究提出了一种聚类适配方法 Cluster-to-Adapt（C2A），通过在转换的特征空间中强制执行聚类目标来选择源和目标域之间可以对齐的类别，从而实现分割数据集领域自适应的跨数据集分类适应。该方法在室内外语境场景下进行的少样本学习和零样本学习中均表现出了比现有方法和基准方法都更好的性能。

聚类到适应：在不同标签之间进行语义分割的少样本领域自适应

Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Segmentation  across Disjoint Labels

Unsupervised domain adaptation (UDA) conventionally assumes labeled source
samples coming from a single underlying source distribution. Whereas in
practical scenario, labeled data are typically collected from diverse sources.
The multiple sources are different not only from the target but also from each
other, thus, domain adaptater should not be modeled in the same way. Moreover,
those sources may not completely share their categories, which further brings a
new transfer challenge called category shift. In this paper, we propose a deep
cocktail network (DCTN) to battle the domain and category shifts among multiple
sources. Motivated by the theoretical results in \cite{mansour2009domain}, the
target distribution can be represented as the weighted combination of source
distributions, and, the multi-source unsupervised domain adaptation via DCTN is
then performed as two alternating steps: i) It deploys multi-way adversarial
learning to minimize the discrepancy between the target and each of the
multiple source domains, which also obtains the source-specific perplexity
scores to denote the possibilities that a target sample belongs to different
source domains. ii) The multi-source category classifiers are integrated with
the perplexity scores to classify target sample, and the pseudo-labeled target
samples together with source samples are utilized to update the multi-source
category classifier and the feature extractor. We evaluate DCTN in three domain
adaptation benchmarks, which clearly demonstrate the superiority of our
framework.

本文提出了一种利用深度混合网络来对抗多源之间的领域和类别差异的方法，通过多种对抗性学习来减小目标域和多源域的差异，并将伪标记的目标样本和源样本用于更新多源分类器和特征提取器。