We generalize the class vectors found in neural networks to linear subspaces
(i.e.~points in the Grassmann manifold) and show that the Grassmann Class
Representation (GCR) enables the simultaneous improvement in accuracy and
feature transferability. In GCR, each class is a subspace and the logit is
defined as the norm of the projection of a feature onto the class subspace. We
integrate Riemannian SGD into deep learning frameworks such that class
subspaces in a Grassmannian are jointly optimized with the rest model
parameters. Compared to the vector form, the representative capability of
subspaces is more powerful. We show that on ImageNet-1K, the top-1 error of
ResNet50-D, ResNeXt50, Swin-T and Deit3-S are reduced by 5.6%, 4.5%, 3.0% and
3.5%, respectively. Subspaces also provide freedom for features to vary and we
observed that the intra-class feature variability grows when the subspace
dimension increases. Consequently, we found the quality of GCR features is
better for downstream tasks. For ResNet50-D, the average linear transfer
accuracy across 6 datasets improves from 77.98% to 79.70% compared to the
strong baseline of vanilla softmax. For Swin-T, it improves from 81.5% to 83.4%
and for Deit3, it improves from 73.8% to 81.4%. With these encouraging results,
we believe that more applications could benefit from the Grassmann class
representation. Code is released at this https URL

广义神经网络中的类向量被推广到线性子空间（即格拉斯曼流形中的点），并展示了格拉斯曼类表示（GCR）能够同时提高准确性和特征可转移性。

兼顾准确性与可迁移性：基于 Grassmann 类别表示的改进

Get the Best of Both Worlds: Improving Accuracy and Transferability by  Grassmann Class Representation

Domain adaptation methods reduce domain shift typically by learning
domain-invariant features. Most existing methods are built on distribution
matching, e.g., adversarial domain adaptation, which tends to corrupt feature
discriminability. In this paper, we propose Discriminative Radial Domain
Adaptation (DRDA) which bridges source and target domains via a shared radial
structure. It's motivated by the observation that as the model is trained to be
progressively discriminative, features of different categories expand outwards
in different directions, forming a radial structure. We show that transferring
such an inherently discriminative structure would enable to enhance feature
transferability and discriminability simultaneously. Specifically, we represent
each domain with a global anchor and each category a local anchor to form a
radial structure and reduce domain shift via structure matching. It consists of
two parts, namely isometric transformation to align the structure globally and
local refinement to match each category. To enhance the discriminability of the
structure, we further encourage samples to cluster close to the corresponding
local anchors based on optimal-transport assignment. Extensively experimenting
on multiple benchmarks, our method is shown to consistently outperforms
state-of-the-art approaches on varied tasks, including the typical unsupervised
domain adaptation, multi-source domain adaptation, domain-agnostic learning,
and domain generalization.

本文提出了判别性径向领域适应方法（DRDA），通过共享径向结构来缩小源域和目标域之间的差异，以增强特征的传递性和可区分性。在多个基准测试中进行了广泛实验，表明该方法在各种任务上均优于现有方法，包括典型的无监督域适应，多源域适应，域不可知学习和域泛化。

判别式径向域自适应

Discriminative Radial Domain Adaptation

Domain adaptation techniques, which focus on adapting models between
distributionally different domains, are rarely explored in the video
recognition area due to the significant spatial and temporal shifts across the
source (i.e. training) and target (i.e. test) domains. As such, recent works on
visual domain adaptation which leverage adversarial learning to unify the
source and target video representations and strengthen the feature
transferability are not highly effective on the videos. To overcome this
limitation, in this paper, we learn a domain-agnostic video classifier instead
of learning domain-invariant representations, and propose an Adversarial
Bipartite Graph (ABG) learning framework which directly models the
source-target interactions with a network topology of the bipartite graph.
Specifically, the source and target frames are sampled as heterogeneous
vertexes while the edges connecting two types of nodes measure the affinity
among them. Through message-passing, each vertex aggregates the features from
its heterogeneous neighbors, forcing the features coming from the same class to
be mixed evenly. Explicitly exposing the video classifier to such cross-domain
representations at the training and test stages makes our model less biased to
the labeled source data, which in-turn results in achieving a better
generalization on the target domain. To further enhance the model capacity and
testify the robustness of the proposed architecture on difficult transfer
tasks, we extend our model to work in a semi-supervised setting using an
additional video-level bipartite graph. Extensive experiments conducted on four
benchmarks evidence the effectiveness of the proposed approach over the SOTA
methods on the task of video recognition.

本文提出一种跨域学习的视频分类器，通过 Adversarial Bipartite Graph 模型学习源域与目标域之间的交互，增强模型的泛化能力，并在半监督学习中扩展了该模型，实验结果表明该方法在视频识别任务上优于当前最佳方法。