Lightweight Probabilistic Deep Networks

Even though probabilistic treatments of neural networks have a long history, they have not found widespread use in practice. Sampling approaches are often too slow already for simple networks. The size of the inputs and the depth of typical CNN architectures in computer vision only compound this problem. Uncertainty in neural networks has thus been largely ignored in practice, despite the fact that it may provide important information about the reliability of predictions and the inner workings of the network. In this paper, we introduce two lightweight approaches to making supervised learning with probabilistic deep networks practical: First, we suggest probabilistic output layers for classification and regression that require only minimal changes to existing networks. Second, we employ assumed density filtering and show that activation uncertainties can be propagated in a practical fashion through the entire network, again with minor changes. Both probabilistic networks retain the predictive power of the deterministic counterpart, but yield uncertainties that correlate well with the empirical error induced by their predictions. Moreover, the robustness to adversarial examples is significantly increased.Comment: To appear at CVPR 201

Neural Nearest Neighbors Networks

Non-local methods exploiting the self-similarity of natural signals have been well studied, for example in image analysis and restoration. Existing approaches, however, rely on k-nearest neighbors (KNN) matching in a fixed feature space. The main hurdle in optimizing this feature space w.r.t. application performance is the non-differentiability of the KNN selection rule. To overcome this, we propose a continuous deterministic relaxation of KNN selection that maintains differentiability w.r.t. pairwise distances, but retains the original KNN as the limit of a temperature parameter approaching zero. To exploit our relaxation, we propose the neural nearest neighbors block (N3 block), a novel non-local processing layer that leverages the principle of self-similarity and can be used as building block in modern neural network architectures. We show its effectiveness for the set reasoning task of correspondence classification as well as for image restoration, including image denoising and single image super-resolution, where we outperform strong convolutional neural network (CNN) baselines and recent non-local models that rely on KNN selection in hand-chosen features spaces.Comment: to appear at NIPS*2018, code available at https://github.com/visinf/n3net

Vulnerability assessment using remote sensing: The earthquake prone megacity Istanbul, Turkey

Hazards like earthquakes are natural, disasters are not. Disasters result from the impact of a hazard on a vulnerable system or society at a specific location. The framework of vulnerability aims at a holistic concept taking physical, environmental, socio-economic and political components into account. This paper focuses on the capabilities of remote sensing to contribute up-to-date spatial information to the physical dimension of vulnerability for the complex urban system of the megacity Istanbul, Turkey. An urban land cover classification based on high resolution satellite data establishes the basis to analyse the spatial distribution of different types of buildings, the carrying capacity of the street network or the identification of open spaces. In addition, a DEM (Digital Elevation Model) enables a localization of potential landslide areas. A methodology to combine these attributes related to the physical dimension of vulnerability is presented. In this process an n-dimensional coordinate system plots the variables describing vulnerability against each other. This enables identification of the degree of vulnerability and the vulnerability-determining factors for a specific location. This assessment of vulnerability provides a broad spatial information basis for decision-makers to develop mitigation strategies