Uniformity testing is arguably one of the most fundamental distribution testing problems.

Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks frequently suffer from a simplicity-biased learning behavior which leads to over-reliance on specific frequency sets, namely as frequency shortcuts, instead of semantic information, resulting in poor generalization performance. Despite previous data augmentation techniques successfully enhancing generalization performances, they intend to apply more frequency shortcuts, thereby causing hallucinations of generalization improvement. In this paper, we aim to prevent such learning behavior of applying frequency shortcuts from a data-driven perspective. Given the theoretical justification of models' biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain. Intuitively, as frequency shortcuts are hidden in the dominant and highly dependent frequencies of dataset structure, dynamically pertur-bating the over-reliance frequency components could prevent the application of frequency shortcuts. To this end, we propose two effective data augmentation modules designed to collaboratively and adaptively adjust the frequency characteristic of the dataset, aiming to dynamically influence the learning behavior of the model and ultimately serving as a strategy to mitigate shortcut learning. Code is available at AdvFrequency .

Tech billionaires seem to be doom prepping. Should we all be worried? Mark Zuckerberg is said to have started work on Koolau Ranch, his sprawling 1,400-acre compound on the Hawaiian island of Kauai, as far back as 2014. It is set to include a shelter, complete with its own energy and food supplies, though the carpenters and electricians working on the site were banned from talking about it by non-disclosure agreements, according to a report by Wired magazine. A six-foot wall blocked the project from view of a nearby road.

Multimodal contrastive learning (MCL) has recently demonstrated significant success across various tasks.