Contrastive learning has gained popularity due to its robustness with good
feature representation performance. However, cosine distance, the commonly used
similarity metric in contrastive learning, is not well suited to represent the
distance between two data points, especially on a nonlinear feature manifold.
Inspired by manifold learning, we propose a novel extension of contrastive
learning that leverages geodesic distance between features as a similarity
metric for histopathology whole slide image classification. To reduce the
computational overhead in manifold learning, we propose geodesic-distance-based
feature clustering for efficient contrastive loss evaluation using prototypes
without time-consuming pairwise feature similarity comparison. The efficacy of
the proposed method is evaluated on two real-world histopathology image
datasets. Results demonstrate that our method outperforms state-of-the-art
cosine-distance-based contrastive learning methods.

提出了一种基于测地线距离的对比学习方法，通过特征聚类减少计算成本，用于组织病理学切片图像分类，结果表明我们的方法优于基于余弦距离的现有方法。

使用深度流形对比学习的组织病理学图像分类

Histopathology Image Classification using Deep Manifold Contrastive  Learning

We propose a simple but effective source-free domain adaptation (SFDA)
method. Treating SFDA as an unsupervised clustering problem and following the
intuition that local neighbors in feature space should have more similar
predictions than other features, we propose to optimize an objective of
prediction consistency. This objective encourages local neighborhood features
in feature space to have similar predictions while features farther away in
feature space have dissimilar predictions, leading to efficient feature
clustering and cluster assignment simultaneously. For efficient training, we
seek to optimize an upper-bound of the objective resulting in two simple terms.
Furthermore, we relate popular existing methods in domain adaptation,
source-free domain adaptation and contrastive learning via the perspective of
discriminability and diversity. The experimental results prove the superiority
of our method, and our method can be adopted as a simple but strong baseline
for future research in SFDA. Our method can be also adapted to source-free
open-set and partial-set DA which further shows the generalization ability of
our method. Code is available in this https URL

提出一种用于领域自适应的无源自适应方法，通过将 SFDA 视为无监督聚类问题并按照特征空间的本地邻居应具有更相似的预测这一直觉来优化预测一致性的目标，进而实现特征聚类及簇分配的高效训练，同时将已有的领域自适应方法、无源域自适应和对比学习之间关系联系起来.

吸引和分散：一种用于无源域自适应的简单方法

Attracting and Dispersing: A Simple Approach for Source-free Domain  Adaptation

Deep learning frameworks allowed for a remarkable advancement in semantic
segmentation, but the data hungry nature of convolutional networks has rapidly
raised the demand for adaptation techniques able to transfer learned knowledge
from label-abundant domains to unlabeled ones. In this paper we propose an
effective Unsupervised Domain Adaptation (UDA) strategy, based on a feature
clustering method that captures the different semantic modes of the feature
distribution and groups features of the same class into tight and
well-separated clusters. Furthermore, we introduce two novel learning
objectives to enhance the discriminative clustering performance: an
orthogonality loss forces spaced out individual representations to be
orthogonal, while a sparsity loss reduces class-wise the number of active
feature channels. The joint effect of these modules is to regularize the
structure of the feature space. Extensive evaluations in the synthetic-to-real
scenario show that we achieve state-of-the-art performance.

本文提出一种基于特征聚类和正交性损失等方法的无监督领域自适应策略，能够在合成到真实场景的情况下有效提高特征空间结构的判别能力并达到最先进的性能。