Titlebook: KI 2014: Advances in Artificial Intelligence; 37th Annual German C Carsten Lutz,Michael Thielscher Conference proceedings 2014 Springer Int

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Environment-Adaptive Learning: How Clustering Helps to Obtain Good Training Dataapply our method to the problem of object detection in indoor environments, for which we use a region-of-interest detector before learning. In experiments we show that our adaptive SSL method can outperform the standard non-adaptive supervised approach on an indoor office data set.

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Fast Semantic Segmentation of RGB-D Scenes with GPU-Accelerated Deep Neural Networksod to provide depth information to convolutional neural networks. For this purpose, we apply a simplified version of the histogram of oriented depth (HOD) descriptor to the depth channel. We evaluate the network on the challenging NYU Depth V2 dataset and show that with our method, we can reach competitive performance at a high frame rate.

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Formula Simplifications as DRAT Derivationsormulas an unsatisfiability proof has to be created. This paper presents certificate constructions for various formula simplification techniques, which are crucial to the success of modern SAT solvers.