2,276 research outputs found
Semi-supervised Embedding in Attributed Networks with Outliers
In this paper, we propose a novel framework, called Semi-supervised Embedding
in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector
representation that systematically captures the topological proximity,
attribute affinity and label similarity of vertices in a partially labeled
attributed network (PLAN). Our method is designed to work in both transductive
and inductive settings while explicitly alleviating noise effects from
outliers. Experimental results on various datasets drawn from the web, text and
image domains demonstrate the advantages of SEANO over state-of-the-art methods
in semi-supervised classification under transductive as well as inductive
settings. We also show that a subset of parameters in SEANO is interpretable as
outlier score and can significantly outperform baseline methods when applied
for detecting network outliers. Finally, we present the use of SEANO in a
challenging real-world setting -- flood mapping of satellite images and show
that it is able to outperform modern remote sensing algorithms for this task.Comment: in Proceedings of SIAM International Conference on Data Mining
(SDM'18
A System-driven Automatic Ground Truth Generation Method for DL Inner-City Driving Corridor Detectors
Data-driven perception approaches are well-established in automated driving
systems. In many fields even super-human performance is reached. Unlike
prediction and planning approaches, mainly supervised learning algorithms are
used for the perception domain. Therefore, a major remaining challenge is the
efficient generation of ground truth data. As perception modules are positioned
close to the sensor, they typically run on raw sensor data of high bandwidth.
Due to that, the generation of ground truth labels typically causes a
significant manual effort, which leads to high costs for the labelling itself
and the necessary quality control. In this contribution, we propose an
automatic labeling approach for semantic segmentation of the drivable ego
corridor that reduces the manual effort by a factor of 150 and more. The
proposed holistic approach could be used in an automated data loop, allowing a
continuous improvement of the depending perception modules.Comment: 8 page
Energy Efficiency Prediction using Artificial Neural Network
Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%
CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains
Unsupervised Domain Adaptation demonstrates great potential to mitigate
domain shifts by transferring models from labeled source domains to unlabeled
target domains. While Unsupervised Domain Adaptation has been applied to a wide
variety of complex vision tasks, only few works focus on lane detection for
autonomous driving. This can be attributed to the lack of publicly available
datasets. To facilitate research in these directions, we propose CARLANE, a
3-way sim-to-real domain adaptation benchmark for 2D lane detection. CARLANE
encompasses the single-target datasets MoLane and TuLane and the multi-target
dataset MuLane. These datasets are built from three different domains, which
cover diverse scenes and contain a total of 163K unique images, 118K of which
are annotated. In addition we evaluate and report systematic baselines,
including our own method, which builds upon Prototypical Cross-domain
Self-supervised Learning. We find that false positive and false negative rates
of the evaluated domain adaptation methods are high compared to those of fully
supervised baselines. This affirms the need for benchmarks such as CARLANE to
further strengthen research in Unsupervised Domain Adaptation for lane
detection. CARLANE, all evaluated models and the corresponding implementations
are publicly available at https://carlanebenchmark.github.io.Comment: 36th Conference on Neural Information Processing Systems (NeurIPS
2022) Track on Datasets and Benchmarks, 22 pages, 11 figure
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
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