Data mixing augmentation has been widely applied to improve the
generalization ability of deep neural networks. Recently, offline data mixing
augmentation, e.g. handcrafted and saliency information-based mixup, has been
gradually replaced by automatic mixing approaches. Through minimizing two
sub-tasks, namely, mixed sample generation and mixup classification in an
end-to-end way, AutoMix significantly improves accuracy on image classification
tasks. However, as the optimization objective is consistent for the two
sub-tasks, this approach is prone to generating consistent instead of diverse
mixed samples, which results in overfitting for target task training. In this
paper, we propose AdAutomixup, an adversarial automatic mixup augmentation
approach that generates challenging samples to train a robust classifier for
image classification, by alternatively optimizing the classifier and the mixup
sample generator. AdAutomixup comprises two modules, a mixed example generator,
and a target classifier. The mixed sample generator aims to produce hard mixed
examples to challenge the target classifier while the target classifier`s aim
is to learn robust features from hard mixed examples to improve generalization.
To prevent the collapse of the inherent meanings of images, we further
introduce an exponential moving average (EMA) teacher and cosine similarity to
train AdAutomixup in an end-to-end way. Extensive experiments on seven image
benchmarks consistently prove that our approach outperforms the state of the
art in various classification scenarios.

通过交替优化分类器和混合样本生成器，我们提出了一种对抗性自动混合增强方法 AdAutomixup，用于生成具有挑战性的样本以训练强大的图像分类器，并通过引入指数移动平均教师和余弦相似度来防止图像内在意义的崩溃。大量实验证明，我们的方法在各种分类场景中优于现有技术水平。

对抗自动混合

Adversarial AutoMixup

Large language models (LLMs) are now available in various sizes and
configurations from cloud API providers. While this diversity offers a broad
spectrum of choices, effectively leveraging the options to optimize
computational cost and performance remains challenging. In this work, we
present AutoMix, an approach that strategically routes queries to larger LMs,
based on the approximate correctness of outputs from a smaller LM. Central to
AutoMix is a few-shot self-verification mechanism, which estimates the
reliability of its own outputs without requiring training. Given that
verifications can be noisy, we employ a meta verifier in AutoMix to refine the
accuracy of these assessments. Our experiments using LLAMA2-13/70B, on five
context-grounded reasoning datasets demonstrate that AutoMix surpasses
established baselines, improving the incremental benefit per cost by up to 89%.
Our code and data are available at this https URL

基于自校验机制和计算成本优化，本研究提出了 AutoMix 方法，通过策略性地将查询导向更大的语言模型，从而超越现有基线模型，提高成本效益。

AutoMix: 自动混合语言模型

AutoMix: Automatically Mixing Language Models

Data mixing augmentation have proved to be effective in improving the
generalization ability of deep neural networks. While early methods mix samples
by hand-crafted policies (e.g., linear interpolation), recent methods utilize
saliency information to match the mixed samples and labels via complex offline
optimization. However, there arises a trade-off between precise mixing policies
and optimization complexity. To address this challenge, we propose a novel
automatic mixup (AutoMix) framework, where the mixup policy is parameterized
and serves the ultimate classification goal directly. Specifically, AutoMix
reformulates the mixup classification into two sub-tasks (i.e., mixed sample
generation and mixup classification) with corresponding sub-networks and solves
them in a bi-level optimization framework. For the generation, a learnable
lightweight mixup generator, Mix Block, is designed to generate mixed samples
by modeling patch-wise relationships under the direct supervision of the
corresponding mixed labels. To prevent the degradation and instability of
bi-level optimization, we further introduce a momentum pipeline to train
AutoMix in an end-to-end manner. Extensive experiments on nine image benchmarks
prove the superiority of AutoMix compared with state-of-the-art in various
classification scenarios and downstream tasks.

该论文提出了一种自动混合框架 ——AutoMix，它将混合分类分为两个子任务，并使用相应的子网络在双层优化框架中解决。AutoMix 在 9 个图像基准上的广泛实验表明，在各种分类场景和下游任务中，其优越性超过了现有技术。