Beyond Finite Data: Towards Data-free Out-of-distribution Generalization via Extrapolation

Out-of-distribution (OOD) generalization is a favorable yet challenging property for deep neural networks. The core challenges lie in the limited availability of source domains that help models learn an invariant representation from the spurious features. Various domain augmentation have been proposed but largely rely on interpolating existing domains and frequently face difficulties in creating truly "novel" domains. Humans, on the other hand, can easily extrapolate novel domains, thus, an intriguing question arises: How can neural networks extrapolate like humans and achieve OOD generalization? We introduce a novel approach to domain extrapolation that leverages reasoning ability and the extensive knowledge encapsulated within large language models (LLMs) to synthesize entirely new domains. Starting with the class of interest, we query the LLMs to extract relevant knowledge for these novel domains. We then bridge the gap between the text-centric knowledge derived from LLMs and the pixel input space of the model using text-to-image generation techniques. By augmenting the training set of domain generalization datasets with high-fidelity, photo-realistic images of these new domains, we achieve significant improvements over all existing methods, as demonstrated in both single and multi-domain generalization across various benchmarks. With the ability to extrapolate any domains for any class, our method has the potential to learn a generalized model for any task without any data. To illustrate, we put forth a much more difficult setting termed, data-free domain generalization, that aims to learn a generalized model in the absence of any collected data. Our empirical findings support the above argument and our methods exhibit commendable performance in this setting, even surpassing the supervised setting by approximately 1-2\% on datasets such as VLCS.Comment: Preprint. Paper under revie

Fabry-Perot interference of Terahertz Pulse radiation

In this summary we show the setup of a THz-TDS system in our new laboratory, Wave Functional Materials Lab. The transmission of a THz pulse radiation is shown in a semiconductor thin film. The Fabry-Perot interference is demonstrated in this ultrathin film by the THz pulse radiation. The theory calculations coincide with the experimental results

Photonic Wannier-Stark Ladder from Coupled Electromagnetic Cavities

We have investigated the photonic Wannier-Stark ladder in the system of coupled electromagnetic cavities, which consists of a stack of metallic plates structured with subwavelength apertures and where the tilted potential effect is mimicked by imposing the gradient variation of refractive index. Making an analogy to its quantum counterpart and assuming the translational property of its solutions, we have shown the photonic ladder has the eigenenergies, that is, frequencies, in a geometrical series. Within the approximation of small gradient, the ladder states manifest the equidistant frequency spacing in the spectrum. By both analytical derivation and numerical simulation, we have illustrated the geometrically progressed energies of the photonic Wannier-Stark ladder

Topology-induced strong diamagnetic response of hollow structured metals at broadband microwave frequencies

We introduce a deep subwavelength aperture with fractal shape and without loading high-index dielectric to the structured metallic plate, and show a strong diamagnetic response