Deep generative approaches have obtained great success in image inpainting
recently. However, most generative inpainting networks suffer from either
over-smooth results or aliasing artifacts. The former lacks high-frequency
details, while the latter lacks semantic structure. To address this issue, we
propose an effective Frequency-Spatial Complementary Network (FSCN) by
exploiting rich semantic information in both spatial and frequency domains.
Specifically, we introduce an extra Frequency Branch and Frequency Loss on the
spatial-based network to impose direct supervision on the frequency
information, and propose a Frequency-Spatial Cross-Attention Block (FSCAB) to
fuse multi-domain features and combine the corresponding characteristics. With
our FSCAB, the inpainting network is capable of capturing frequency information
and preserving visual consistency simultaneously. Extensive quantitative and
qualitative experiments demonstrate that our inpainting network can effectively
achieve superior results, outperforming previous state-of-the-art approaches
with significantly fewer parameters and less computation cost. The code will be
released soon.

该研究提出了一种有效的频空间补偿网络 (Frequency-Spatial Complementary Network)，通过利用空间和频率域中丰富的语义信息建立了额外的频率支路和频率损失，并提出了一种频率 - 空间跨域注意力块 (Frequency-Spatial Cross-Attention Block) 来融合多域特征和结合对应的特征。通过测试和量化实验，证明了该方法在保留高频细节、语义结构和视觉一致性方面表现优异。

空间和频率提示对高保真图像修复的贡献

Both Spatial and Frequency Cues Contribute to High-Fidelity Image  Inpainting

Molecule generation is a challenging open problem in cheminformatics.
Currently, deep generative approaches addressing the challenge belong to two
broad categories, differing in how molecules are represented. One approach
encodes molecular graphs as strings of text, and learns their corresponding
character-based language model. Another, more expressive, approach operates
directly on the molecular graph. In this work, we address two limitations of
the former: generation of invalid and duplicate molecules. To improve validity
rates, we develop a language model for small molecular substructures called
fragments, loosely inspired by the well-known paradigm of Fragment-Based Drug
Design. In other words, we generate molecules fragment by fragment, instead of
atom by atom. To improve uniqueness rates, we present a frequency-based masking
strategy that helps generate molecules with infrequent fragments. We show
experimentally that our model largely outperforms other language model-based
competitors, reaching state-of-the-art performances typical of graph-based
approaches. Moreover, generated molecules display molecular properties similar
to those in the training sample, even in absence of explicit task-specific
supervision.

通过采用 Fragment-Based Drug Design 范例生成小分子结构语言模型并进行分子生成，大幅提高了分子的有效性和独特性，达到了基于图像的模型的最佳表现。