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Das TMKB-Modell beschreibt einen Weg, den persönlichen und unternehmerischen Erfolg im beruflichen Alltag effizient und nachhaltig zu erreichen. Hierbei steht TMKB für den Transfer von Methoden und Kompetenzen in den Beruf. Dieses Modell beachtet die Theorien der Kompetenzentwicklung im Kontext realer Problemstellungen in Unternehmen. Beispielhaft wird das TMKB-Modell in der ersten Stufe mit dem Schwerpunkt Lean- und Projektmanagement erläutert. Die Zielgruppe dieses Ansatzes lässt sich über die der Auszubildenden bis hin zu Studierenden und Berufserfahrenen erweitern.
Financing trade and development sustainably will be crucial for Africa. Enhanced collaboration between multilateral development banks, development finance institutions and ECAs could greatly enhance intra-regional trade. Furthermore, setting up a ‘level playing field’ on the continent will allow governments to make strategic interventions for successful export credits and trade finance solutions, fostering growth through trade. African trade is already showing signs of rebounding from the coronavirus- induced recession. Through concerted, co-operative and continent-wide efforts, drawing on the knowledge and resources of all types of institutions and policy experts, Africa will continue to grow confidently and quickly into its increasingly important role as an engine of economic growth and global trade.
Die wichtigste Erfahrung beim Zeichnen ist der Prozess, mit einem Stift Spuren und Zeichen zu setzen, die direkt beim Zeichnen entstehen. Ob mit Stift auf Papier, mit dem Finger oder einem Stock im Sand: Man lässt sich auf diesen Prozess des Entstehens ein. Es ist ein Wechselspiel von Auge und Hand, mal gewollt und kontrolliert, ein anderes Mal als Spiel aus Neugier, Intuition und Zufall. Wenn es gelingt, den Alltag auszuschließen, ist Zeichnen wie Musizieren oder Tanzen, ein Akt der Poiesis, das Hervorbringen von Werken im autotelischen Zustand. Handeln und Sein ist als Qualität und Erkenntnisform eins oder neudeutsch: Ich bin im Flow.
The term attribute transfer refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes. Prominent example applications are photo realistic changes of facial features and expressions, like changing the hair color, adding a smile, enlarging the nose or altering the entire context of a scene, like transforming a summer landscape into a winter panorama. Recent advances in attribute transfer are mostly based on generative deep neural networks, using various techniques to manipulate images in the latent space of the generator.
In this paper, we present a novel method for the common sub-task of local attribute transfers, where only parts of a face have to be altered in order to achieve semantic changes (e.g. removing a mustache). In contrast to previous methods, where such local changes have been implemented by generating new (global) images, we propose to formulate local attribute transfers as an inpainting problem. Removing and regenerating only parts of images, our Attribute Transfer Inpainting Generative Adversarial Network (ATI-GAN) is able to utilize local context information to focus on the attributes while keeping the background unmodified resulting in visually sound results.
Generative adversarial networks are the state of the art approach towards learned synthetic image generation. Although early successes were mostly unsupervised, bit by bit, this trend has been superseded by approaches based on labelled data. These supervised methods allow a much finer-grained control of the output image, offering more flexibility and stability. Nevertheless, the main drawback of such models is the necessity of annotated data. In this work, we introduce an novel framework that benefits from two popular learning techniques, adversarial training and representation learning, and takes a step towards unsupervised conditional GANs. In particular, our approach exploits the structure of a latent space (learned by the representation learning) and employs it to condition the generative model. In this way, we break the traditional dependency between condition and label, substituting the latter by unsupervised features coming from the latent space. Finally, we show that this new technique is able to produce samples on demand keeping the quality of its supervised counterpart.
Generative adversarial networks (GANs) provide state-of-the-art results in image generation. However, despite being so powerful, they still remain very challenging to train. This is in particular caused by their highly non-convex optimization space leading to a number of instabilities. Among them, mode collapse stands out as one of the most daunting ones. This undesirable event occurs when the model can only fit a few modes of the data distribution, while ignoring the majority of them. In this work, we combat mode collapse using second-order gradient information. To do so, we analyse the loss surface through its Hessian eigenvalues, and show that mode collapse is related to the convergence towards sharp minima. In particular, we observe how the eigenvalues of the G are directly correlated with the occurrence of mode collapse. Finally, motivated by these findings, we design a new optimization algorithm called nudged-Adam (NuGAN) that uses spectral information to overcome mode collapse, leading to empirically more stable convergence properties.