Abstract: The invention provides a method for adapting a text-to-image (T2I) diffusion model for video editing by using spectral decomposition to achieve controlled spectral shifts in the model's weights. This adaptation involves maintaining constant singular vectors while selectively adjusting singular values in response to a text prompt. A spectral shift regularizer constrains adjustments, particularly limiting changes to larger singular values to ensure minimal deviation from the original model's structure. This approach allows efficient, prompt-driven video editing by modifying specific elements according to the prompt while preserving the original video context. By focusing on selective spectral adjustments, the method reduces adaptation time and computational demands, making it suitable for real-time and resource-sensitive applications, such as dynamic video editing for streaming services.

Abstract: The invention provides a method for adapting a text-to-image (T2I) diffusion model for video editing by using spectral decomposition to achieve controlled spectral shifts in the model's weights. This adaptation involves maintaining constant singular vectors while selectively adjusting singular values in response to a text prompt. A spectral shift regularizer constrains adjustments, particularly limiting changes to larger singular values to ensure minimal deviation from the original model's structure. This approach allows efficient, prompt-driven video editing by modifying specific elements according to the prompt while preserving the original video context. By focusing on selective spectral adjustments, the method reduces adaptation time and computational demands, making it suitable for real-time and resource-sensitive applications, such as dynamic video editing for streaming services.

Abstract: In general, techniques are described for unsupervised domain adaptation of models with pseudo-label curation. In an example, a method includes generating a plurality of pseudo-labels for a dataset of unlabeled data using a source machine learning model; estimating a reliability of each pseudo-label of the plurality of pseudo-labels using one or more reliability measures; selecting a subset of the plurality of pseudo-labels having estimated reliabilities that satisfy a reliability threshold; and training, using one or more curriculum learning techniques, a target machine learning model starting with the selected subset of the plurality of pseudo-labels and the corresponding unlabeled data.

Abstract: The present invention relates to electrically conductive materials. The present invention also relates to processes for the preparation of these materials and to electronic circuits, electronic devices and textile garments that comprise them.