Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging

We consider the problem of reconstructing signals and images from periodic nonlinearities. For such problems, we design a measurement scheme that supports efficient reconstruction; moreover, our method can be adapted to extend to compressive sensing-based signal and image acquisition systems. Our techniques can be potentially useful for reducing the measurement complexity of high dynamic range (HDR) imaging systems, with little loss in reconstruction quality. Several numerical experiments on real data demonstrate the effectiveness of our approach

MultiStyleGAN: Multiple One-shot Face Stylizations using a Single GAN

Image stylization aims at applying a reference style to arbitrary input images. A common scenario is one-shot stylization, where only one example is available for each reference style. A successful recent approach for one-shot face stylization is JoJoGAN, which fine-tunes a pre-trained StyleGAN2 generator on a single style reference image. However, it cannot generate multiple stylizations without fine-tuning a new model for each style separately. In this work, we present a MultiStyleGAN method that is capable of producing multiple different face stylizations at once by fine-tuning a single generator. The key component of our method is a learnable Style Transformation module that takes latent codes as input and learns linear mappings to different regions of the latent space to produce distinct codes for each style, resulting in a multistyle space. Our model inherently mitigates overfitting since it is trained on multiple styles, hence improving the quality of stylizations. Our method can learn upwards of $12$ image stylizations at once, bringing upto $8\times$ improvement in training time. We support our results through user studies that indicate meaningful improvements over existing methods

Algorithms for solving inverse problems using generative models

The traditional approach of hand-crafting priors (such as sparsity) for solving inverse problems is slowly being replaced by the use of richer learned priors (such as those modeled by generative adversarial networks, or GANs). In this work, we study the algorithmic aspects of such a learning-based approach from a theoretical perspective. For certain generative network architectures, we establish a simple non-convex algorithmic approach that (a) theoretically enjoys linear convergence guarantees for certain linear and nonlinear inverse problems, and (b) empirically improves upon conventional techniques such as back-propagation. We support our claims with the experimental results for solving various inverse problems. We also propose an extension of our approach that can handle model mismatch (i.e., situations where the generative network prior is not exactly applicable.) Together, our contributions serve as building blocks towards a principled use of generative models in inverse problems with more complete algorithmic understanding

Evolving therapeutics in acid-related disorders of the gut: is vonoprazan the drug of the next decade

Vonoprazan a competitive potassium acid blocker (P-CAB) has been gaining traction compared to the use of traditional PPIs for the treatment of GERD and other acid-related disorders. We aim to review its comparative edge over the other widely used PPIs and its combination therapies in this article. It is also emerging to show superiority in mucosal and clinical healing rates in the already published literature. The mechanism of action of vonoprazan involves the antagonism of potassium channels present in parietal cells, thereby inhibiting gastric acid secretion. This distinctive mode of action effectively suppresses acid secretion in gastroesophageal reflux disease (GERD) and erosive esophagitis.  For clinical usage, 20 mg of vonoprazan has been reported to be more effective in the management of acid-related disorders compared to conventional PPIs. This article underscores the need for continued research to fully elucidate the potential of vonoprazan in managing acid-related disorders, particularly in populations beyond Japan where its utility remains to be fully explored. It also aims to enlighten healthcare providers regarding its usage in the management of acid-related gastrointestinal disorders by providing insights into its clinical utility, practical considerations, & effective drug combinations eventually ameliorating patient outcomes and quality of life

ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs

Methods for finetuning generative models for concept-driven personalization generally achieve strong results for subject-driven or style-driven generation. Recently, low-rank adaptations (LoRA) have been proposed as a parameter-efficient way of achieving concept-driven personalization. While recent work explores the combination of separate LoRAs to achieve joint generation of learned styles and subjects, existing techniques do not reliably address the problem; they often compromise either subject fidelity or style fidelity. We propose ZipLoRA, a method to cheaply and effectively merge independently trained style and subject LoRAs in order to achieve generation of any user-provided subject in any user-provided style. Experiments on a wide range of subject and style combinations show that ZipLoRA can generate compelling results with meaningful improvements over baselines in subject and style fidelity while preserving the ability to recontextualize. Project page: https://ziplora.github.ioComment: Project page: https://ziplora.github.i