The Conditional Analogy GAN: Swapping Fashion Articles on People Images

We present a novel method to solve image analogy problems : it allows to learn the relation between paired images present in training data, and then generalize and generate images that correspond to the relation, but were never seen in the training set. Therefore, we call the method Conditional Analogy Generative Adversarial Network (CAGAN), as it is based on adversarial training and employs deep convolutional neural networks. An especially interesting application of that technique is automatic swapping of clothing on fashion model photos. Our work has the following contributions. First, the definition of the end-to-end trainable CAGAN architecture, which implicitly learns segmentation masks without expensive supervised labeling data. Second, experimental results show plausible segmentation masks and often convincing swapped images, given the target article. Finally, we discuss the next steps for that technique: neural network architecture improvements and more advanced applications.Comment: To appear at the International Conference on Computer Vision, ICCV 2017, Workshop on Computer Vision for Fashio

Consistency Learning and Multiple Rankings Combination for Text Retrieval

Text retrieval is one of the most basic tasks in the field of information retrieval. This paper deals with retrieving relevant documents for text-based queries from a database. Several different methods for retrieving text are explored, and show widely differing performance on different queries. It is shown how each of those methods may be improved through a � consistency learning � framework, where properties of the database and similarities on three different levels, namely documents, words and synonym sets, are exploited to improve performance. Further gains are achieved when all of the basic functions are combined in a metamodel to get better retrieval accuracy than each of the individual models. Categories and Subject Descriptor

Analyse und Anwendung in Roboterplanung und -Regelung

Generating motion is a crucial aspect of articulated robotics. Many robot manipulation tasks can be defined and solved as a motion trajectory generation problem where the robot needs to calculate and execute a task-appropriate movement. Multiple efficient methods have been developed for this problem in the robotic community, but they do not make use of the patterns found in motion data. We will propose in this thesis novel approaches to combine machine learning and robotics. The main conceptual achievement of this thesis is the successful fusion of machine learning and robotic algorithms to improve motion generation and discover the structure of different motion tasks. By observing examples of robot motions, optimal for a given task, we can find the relevant features of the motions, and discover the latent structure inherent in the interaction between robot and workspace. This leads to new algorithms for planning and control which are demonstrated in numerous experiments to improve on previous approaches in terms of speed and generalization ability. For speeding up motion planning we developed an algorithm called Trajectory Prediction. In the trajectory planning scenario the desired robot behavior is specified by a cost function and a planner algorithm is used to generate low- cost motions. Our contribution is to predict an appropriate initial trajectory that can speed up the planner. We do this by learning a mapping from situation to trajectory, and extracting the representations useful for such a mapping. For learning from demonstration we developed an algorithm called Task Space Retrieval Using Inverse Optimal Control. In this scenario no cost function is available to spec- ify what is a good motion. By observing example trajectories our method can learn a value function model and an efficient sparse task space representation of the desired behavior. A controller for motion generation is developed based on this value function, effectively generalizing the demonstrated behavior in novel situations.Ein Schwerpunkt der Forschung an Robotern ist die Generierung von Bewegung. Um die Gegenstände in seiner Umgebung handzuhaben und mit ihnen verschiedene Auf- gaben zu lösen, muss ein Roboter die dafür notwendigen Bewegungtrajektorien berech- nen und anschliessend ausführen. Eine Vielzahl Methoden zur Generierung von Bewe- gung wurde in der Robotik entwickelt. Doch keine dieser Methoden nutzt die Muster in vorangegangenen erfolgreichen Bewegungen aus, um die Bewegungsgenerierung besser und schneller zu machen. In dieser Doktorarbeit schlage ich neuartige Ansätze vor, die Techniken des Maschinellen Lernens und der Robotik vereinen. Diese Ansätze er- möglichen es einem Roboter, die Merkmale und latente Struktur in seiner Interaktion mit der Umgebung zu analysieren und dadurch aus seiner Erfahrung effizientere Bewe- gungen zu lernen. In zahlreichen Experimenten zeige ich, dass meine Ansätze schneller und effizienter Bewegungen erzeugen als etablierte Techniken. Der erste konkrete Beitrag zu Bewegungsplanung ist die so genannte Trajectory Pre- diction. Unsere Methode kann, gegeben eine Weltbeschreibung, schnell eine geeignete Trajektorie, aufgrund einer von Daten gelernten Funktion, vorhersagen. Die von Daten gelernten Muster erlauben eine deutlich schnellere Bewegungserzeugung in Vergleich zu Methoden die diese Daten nicht benutzen. Diese Muster reflektieren Strukturen in der Anordnung von Objekte, die die robotische Bewegung beinflussen. Mein zweiter Ansatz Task Space Retrieval using Inverse Optimal Control ist eine Meth- ode zum Lernen aus der Beobachtung einer Bewegungsdemonstration. Aus der Demon- stration lernt dieser Ansatz eine Wertefunktion für gute Bewegungen und eine kompakte Aufgabenrepräsentation. Dadurch kann ein Roboter auf Grundlage der gelernten Werte- funktion in neuen Situationen angemessene Bewegungen erzeugen und sich wie demon- striert verhalten, ohne dass ihm eine explizite Aufgabenspezifikation gegeben werden muss

Trajectory prediction in Cluttered Voxel Environments

Abstract — Trajectory planning and optimization is a fundamental problem in articulated robotics. It is often viewed as a two phase problem of initial feasible path planning around obstacles and subsequent optimization of a trajectory satisfying dynamical constraints. There are many methods that can generate good movements when given enough time, but planning for high-dimensional robot configuration spaces in realistic environments with many objects in real time remains challenging. This work presents a novel way for faster movement planning in such environments by predicting good path initializations. We build on our previous work on trajectory prediction by adapting it to environments modeled with voxel grids and defining a frame invariant prototype trajectory space. The constructed representations can generalize to a wide range of situations, allowing to predict good movement trajectories and speed up convergence of robot motion planning. An empirical comparison of the effect on planning movements with a combination of different trajectory initializations and local planners is presented and tested on a Schunk arm manipulation platform with laser sensors in simulation and hardware. I