SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects

Recent advances in image generation gave rise to powerful tools for semantic image editing. However, existing approaches can either operate on a single image or require an abundance of additional information. They are not capable of handling the complete set of editing operations, that is addition, manipulation or removal of semantic concepts. To address these limitations, we propose SESAME, a novel generator-discriminator pair for Semantic Editing of Scenes by Adding, Manipulating or Erasing objects. In our setup, the user provides the semantic labels of the areas to be edited and the generator synthesizes the corresponding pixels. In contrast to previous methods that employ a discriminator that trivially concatenates semantics and image as an input, the SESAME discriminator is composed of two input streams that independently process the image and its semantics, using the latter to manipulate the results of the former. We evaluate our model on a diverse set of datasets and report state-of-the-art performance on two tasks: (a) image manipulation and (b) image generation conditioned on semantic labels

Rewriting a Deep Generative Model

A deep generative model such as a GAN learns to model a rich set of semantic and physical rules about the target distribution, but up to now, it has been obscure how such rules are encoded in the network, or how a rule could be changed. In this paper, we introduce a new problem setting: manipulation of specific rules encoded by a deep generative model. To address the problem, we propose a formulation in which the desired rule is changed by manipulating a layer of a deep network as a linear associative memory. We derive an algorithm for modifying one entry of the associative memory, and we demonstrate that several interesting structural rules can be located and modified within the layers of state-of-the-art generative models. We present a user interface to enable users to interactively change the rules of a generative model to achieve desired effects, and we show several proof-of-concept applications. Finally, results on multiple datasets demonstrate the advantage of our method against standard fine-tuning methods and edit transfer algorithms.Comment: ECCV 2020 (oral). Code at https://github.com/davidbau/rewriting. For videos and demos see https://rewriting.csail.mit.edu

The Transcriptome of the Nosocomial Pathogen Enterococcus faecalis V583 Reveals Adaptive Responses to Growth in Blood

gains access to the bloodstream and establishes a persistent infection is not well understood.. infections

Structural heterogeneity of membrane receptors and GTP-binding proteins and its functional consequences for signal transduction

Boege F, Neumann E, Helmreich EJM. Structural heterogeneity of membrane receptors and GTP-binding proteins and its functional consequences for signal transduction. European Journal of Biochemistry. 1991;199(1):1-15.Recent information obtained, mainly by recombinant cDNA technology, on structural heterogeneity of hormone and transmitter receptors, of GTP-binding proteins (G-proteins) and, especially, of G-protein-linked receptors is reviewed and the implications of structural heterogeneity for diversity of hormone and transmitter actions is discussed. For the future, three-dimensional structural analysis of membrane proteins participating in signal transmission and transduction pathways is needed in order to understand the molecular basis of allosteric regulatory mechanisms governing the interactions between these proteins including hysteretic properties and cell-cybernetic aspects