Current solutions to discriminative and generative tasks in computer vision exist separately and often lack interpretability and explainability. Using faces as our application domain, here we present an architecture that is based around two core ideas that address these issues: first, our framework learns an unsupervised, low-dimensional embedding of faces using an adversarial autoencoder that is able to synthesize high-quality face images. Second, a supervised disentanglement splits the low-dimensional embedding vector into four sub-vectors, each of which contains separated information about one of four major face attributes (pose, identity, expression, and style) that can be used both for discriminative tasks and for manipulating all four attributes in an explicit manner. The resulting architecture achieves state-of-the-art image quality, good discrimination and face retrieval results on each of the four attributes, and supports various face editing tasks using a face representation of only 99 dimensions. Finally, we apply the architecture's robust image synthesis capabilities to visually debug label-quality issues in an existing face dataset.
Organizers: Timo Bolkart
Bioelectronics integrates principles of electrical engineering and materials science to biology, medicine and ultimately health. Soft bioelectronics focus on designing and manufacturing electronic devices with mechanical properties close to those of the host biological tissue so that long-term reliability and minimal perturbation are induced in vivo and/or truly wearable systems become possible. We illustrate the potential of this soft technology with examples ranging from prosthetic tactile skins to soft multimodal neural implants.
Vaccine refusal can lead to outbreaks of previously eradicated diseases and is an increasing problem worldwide. Vaccinating decisions exemplify a complex, coupled system where vaccinating behavior and disease dynamics influence one another. Complex systems often exhibit characteristic dynamics near a tipping point to a new dynamical regime. For instance, critical slowing down -- the tendency for a system to start `wobbling'-- can increase close to a tipping point. We used a linear support vector machine to classify the sentiment of geo-located United States and California tweets concerning measles vaccination from 2011 to 2016. We also extracted data on internet searches on measles from Google Trends. We found evidence for critical slowing down in both datasets in the years before and after the 2014-15 Disneyland, California measles outbreak, suggesting that the population approached a tipping point corresponding to widespread vaccine refusal, but then receded from the tipping point in the face of the outbreak. A differential equation model of coupled behaviour-disease dynamics is shown to illustrate the same patterns. We conclude that studying critical phenomena in online social media data can help us develop analytical tools based on dynamical systems theory to identify populations at heightened risk of widespread vaccine refusal.
This talk will look at hardware-based means of assembling, controlling and driving systems at the smallest of scales, including those that can become autonomous. I will show that insights from physics, chemistry and material engineering can be used to permit the simplification and miniaturization of otherwise bulky systems and that this can give rise to new technologies. One of the technologies we have invented may also permit the development of new imaging devices.
In multi-task learning, a learner is given a collection of prediction tasks and needs to solve all of them. In contrast to previous work, that required that annotated training data must be available for all tasks, I will talk about a new setting, in which for some tasks, potentially most of them, only unlabeled training data is available. Consequently, to solve all tasks, information must be transfered between tasks with labels and tasks without labels. Focussing on an instance-based transfer method I will consider two variants of this setting: when the set of labeled tasks is fixed, and when it can be actively selected by the learner. I will discuss a generalization bound that covers both scenarios and an algorithm, that follows from it, for making the choice of labeled tasks (in the active case) and for transferring information between the tasks in a principled way. I will also show results of some experiments that illustrate the effectiveness of the algorithm.
Organizers: Georg Martius
This talk draws three parallels between classical algebraic quadrature rules, that are exact for polynomials of low degree, and kernel (or Bayesian) quadrature rules: i) Computational efficiency. Construction of scalable multivariate algebraic quadrature rules is challenging whereas kernel quadrature necessitates solving a linear system of equations, quickly becoming computationally prohibitive. Fully symmetric sets and Smolyak sparse grids can be used to solve both problems. ii) Derivatives and optimal rules. Algebraic degree of a Gaussian quadrature rule cannot be improved by adding derivative evaluations of the integrand. This holds for optimal kernel quadrature rules in the sense that derivatives are of no help in minimising the worst-case error (or posterior integral variance). iii) Positivity of the weights. Essentially as a consequence of the preceding property, both the Gaussian and optimal kernel quadrature rules have positive weights (i.e., they are positive linear functionals).
Organizers: Alexandra Gessner
Standard methods of causal discovery take as input a statistical data set of measurements of well-defined causal variables. The goal is then to determine the causal relations among these variables. But how are these causal variables identified or constructed in the first place? Often we have sensor level data but assume that the relevant causal interactions occur at a higher scale of aggregation. Sometimes we only have aggregate measurements of causal interactions at a finer scale. I will motivate the general problem of causal discovery and present recent work on a framework and method for the construction and identification of causal macro-variables that ensures that the resulting causal variables have well-defined intervention distributions. Time permitting, I will show an application of this approach to large scale climate data, for which we were able to identify the macro-phenomenon of El Nino using an unsupervised method on micro-level measurements of the sea surface temperature and wind speeds over the equatorial Pacific.
Organizers: Sebastian Weichwald
This work investigates the development of the sense of agency and of object permanence in humanoid robots. Based on findings from developmental psychology and from neuroscience, development of sense of object permanence is linked to development of sense of agency and to processes of internal simulation of sensor activity. In the course of the work, two sets of experiments will be presented, in the first set a humanoid robot has to learn the forward relationship between its movements and their sensory consequences perceived from the visual input. In particular, a self-monitoring mechanism was implemented that allows the robot to distinguish between self-generated movements and those generated by external events. In a second experiment, once having learned this mapping, the self-monitoring mechanism is exploited to suppress the predicted visual consequences of intended movements. The speculation is made that this process can allow for the development of sense of object permanence. It will be shown, that using these predictions, the robot maintains an enhanced simulated image where an object occluded by the movement of the robot arm is still visible, due to sensory attenuation processes.
In robotics, it is often practically and theoretically convenient to design motion planners for approximate simple robot and environment models first, and then adapt such reference planners to more accurate complex settings. In this talk, I will introduce a new approach to extend the applicability of motion planners of simple settings to more complex settings using reference governors. Reference governors are add-on control schemes for closed-loop dynamical systems to enforce constraint satisfaction while maintaining stability, and offers a systematic way of separating the issues of stability and constraint enforcement. I will demonstrate example applications of reference governors for sensor-based navigation in environments cluttered with convex obstacles and for smooth extensions of low-order (e.g., position- or velocity-controlled) feedback motion planners to high-order (e.g., force/torque controlled) robot models, while retaining stability and collision avoidance properties.
Growth of the internet and social media has spurred the sharing and dissemination of personal data at large scale. At the same time, recent developments in computer vision has enabled unseen effectiveness and efficiency in automated recognition. It is clear that visual data contains private information that can be mined, yet the privacy implications of sharing such data have been less studied in computer vision community. In the talk, I will present some key results from our study of the implications of the development of computer vision on the identifiability in social media, and an analysis of existing and new anonymisation techniques. In particular, we show that adversarial image perturbations (AIP) introduce human invisible perturbations on the input image that effectively misleads a recogniser. They are far more aesthetic and effective compared to e.g. face blurring. The core limitation, however, is that AIPs are usually generated against specific target recogniser(s), and it is hard to guarantee the performance against uncertain, potentially adaptive recognisers. As a first step towards dealing with the uncertainty, we have introduced a game theoretical framework to obtain the user’s privacy guarantee independent of the randomly chosen recogniser (within some fixed set).
Organizers: Siyu Tang