In recent years, discriminative self-supervised methods have made significant
strides in advancing various visual tasks. The central idea of learning a data
encoder that is robust to data distortions/augmentations is straightforward yet
highly effective. Although many studies have demonstrated the empirical success
of various learning methods, the resulting learned representations can exhibit
instability and hinder downstream performance. In this study, we analyze
discriminative self-supervised methods from a causal perspective to explain
these unstable behaviors and propose solutions to overcome them. Our approach
draws inspiration from prior works that empirically demonstrate the ability of
discriminative self-supervised methods to demix ground truth causal sources to
some extent. Unlike previous work on causality-empowered representation
learning, we do not apply our solutions during the training process but rather
during the inference process to improve time efficiency. Through experiments on
both controlled image datasets and realistic image datasets, we show that our
proposed solutions, which involve tempering a linear transformation with
controlled synthetic data, are effective in addressing these issues.

从因果关系的角度分析了有问题的自我监督方法，并提出了解决方案，通过在推理过程中应用线性变换来提高时间效率。

稳定且因果推理用于区分自监督深度视觉表示

Stable and Causal Inference for Discriminative Self-supervised Deep  Visual Representations

Existing federated classification algorithms typically assume the local
annotations at every client cover the same set of classes. In this paper, we
aim to lift such an assumption and focus on a more general yet practical
non-IID setting where every client can work on non-identical and even disjoint
sets of classes (i.e., client-exclusive classes), and the clients have a common
goal which is to build a global classification model to identify the union of
these classes. Such heterogeneity in client class sets poses a new challenge:
how to ensure different clients are operating in the same latent space so as to
avoid the drift after aggregation? We observe that the classes can be described
in natural languages (i.e., class names) and these names are typically safe to
share with all parties. Thus, we formulate the classification problem as a
matching process between data representations and class representations and
break the classification model into a data encoder and a label encoder. We
leverage the natural-language class names as the common ground to anchor the
class representations in the label encoder. In each iteration, the label
encoder updates the class representations and regulates the data
representations through matching. We further use the updated class
representations at each round to annotate data samples for locally-unaware
classes according to similarity and distill knowledge to local models.
Extensive experiments on four real-world datasets show that the proposed method
can outperform various classical and state-of-the-art federated learning
methods designed for learning with non-IID data.

本文提出了一种基于自然语言类名（class names）在非同分布（non-IID）数据上进行联邦学习（federated learning）的方法，将分类问题转化为数据表示和类名表示之间的匹配，并将分类模型分为数据编码器和标签编码器进行处理，通过更新类别表示来提高该算法在客户端独占类（client-exclusive classes）场景下的分类准确性。