Outlier exposure (OE) is powerful in out-of-distribution (OOD) detection,
enhancing detection capability via model fine-tuning with surrogate OOD data.
However, surrogate data typically deviate from test OOD data. Thus, the
performance of OE, when facing unseen OOD data, can be weakened. To address
this issue, we propose a novel OE-based approach that makes the model perform
well for unseen OOD situations, even for unseen OOD cases. It leads to a
min-max learning scheme -- searching to synthesize OOD data that leads to worst
judgments and learning from such OOD data for uniform performance in OOD
detection. In our realization, these worst OOD data are synthesized by
transforming original surrogate ones. Specifically, the associated transform
functions are learned implicitly based on our novel insight that model
perturbation leads to data transformation. Our methodology offers an efficient
way of synthesizing OOD data, which can further benefit the detection model,
besides the surrogate OOD data. We conduct extensive experiments under various
OOD detection setups, demonstrating the effectiveness of our method against its
advanced counterparts.

通过最大最小学习策略，利用模型扰动产生一类最差情况的数据来合成新的数据，以优化 outlier exposure 在 out-of-distribution 检测中的性能。

通过隐式异常转换进行的越界检测

Out-of-distribution Detection with Implicit Outlier Transformation

To lower the communication complexity of federated min-max learning, a
natural approach is to utilize the idea of infrequent communications (through
multiple local updates) same as in conventional federated learning. However,
due to the more complicated inter-outer problem structure in federated min-max
learning, theoretical understandings of communication complexity for federated
min-max learning with infrequent communications remain very limited in the
literature. This is particularly true for settings with non-i.i.d. datasets and
partial client participation. To address this challenge, in this paper, we
propose a new algorithmic framework called stochastic sampling averaging
gradient descent ascent (SAGDA), which i) assembles stochastic gradient
estimators from randomly sampled clients as control variates and ii) leverages
two learning rates on both server and client sides. We show that SAGDA achieves
a linear speedup in terms of both the number of clients and local update steps,
which yields an $\mathcal{O}(\epsilon^{-2})$ communication complexity that is
orders of magnitude lower than the state of the art. Interestingly, by noting
that the standard federated stochastic gradient descent ascent (FSGDA) is in
fact a control-variate-free special version of SAGDA, we immediately arrive at
an $\mathcal{O}(\epsilon^{-2})$ communication complexity result for FSGDA.
Therefore, through the lens of SAGDA, we also advance the current understanding
on communication complexity of the standard FSGDA method for federated min-max
learning.

本文提出了一种名为 SAGDA 的新算法框架，用于降低联邦 min-max 学习的通信复杂度，并在此基础上提高了对标准 FSGDA 方法通信复杂度的理解。