Predicting compositional changes of organic–inorganic hybrid materials with Augmented CycleGAN
نویسندگان
چکیده
Image-to-image translation models applied to materials: augmented CycleGAN for predicting chemical compositions of hybrid materials.
منابع مشابه
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data
Learning inter-domain mappings from unpaired data can improve performance in structured prediction tasks, such as image segmentation, by reducing the need for paired data. CycleGAN was recently proposed for this problem, but critically assumes the underlying inter-domain mapping is approximately deterministic and one-to-one. This assumption renders the model ineffective for tasks requiring flex...
متن کاملSynthesis and properties of soft nanocomposite materials with novel organicinorganic network structures
We have fabricated new types of polymer hydrogels and polymer nanocomposites, that is, nanocomposite gels (NC gels) and soft polymer nanocomposites (M-NCs), with novel organic/inorganic network structures. Both NC gels and M-NCs were synthesized by in situ free-radical polymerization in the presence of exfoliated clay platelets in aqueous systems and were obtained in various forms and sizes wit...
متن کاملCycleGAN, a Master of Steganography
CycleGAN [Zhu et al., 2017] is one recent successful approach to learn a transformation between two image distributions. In a series of experiments, we demonstrate an intriguing property of the model: CycleGAN learns to “hide” information about a source image into the images it generates in a nearly imperceptible, highfrequency signal. This trick ensures that the generator can recover the origi...
متن کاملCycleGAN Face-off
Face-off is an interesting case of style transfer where the facial expressions and attributes of one person could be fully transformed to another face. We are interested in the unsupervised training process which only requires two sequences of unaligned video frames from each person and learns what shared attributes to extract automatically. In this project, we explored various improvements for...
متن کاملComprehensive Compositional Imaging of Heterogeneous Materials with Atomic Force Microscopy
Distinguishing of individual constituents in complex materials is one of the primary tasks of microscopy that is achieved by visualization of specific shapes and features of components. Electron microscopy and atomic force microscopy (AFM) [1] extend visualization of objects down to the atomic scale thus enhancing compositional imaging. The situation becomes less trivial when the constituents l...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Digital discovery
سال: 2022
ISSN: ['2635-098X']
DOI: https://doi.org/10.1039/d1dd00044f