17,601 research outputs found
Anchor Loss: Modulating Loss Scale Based on Prediction Difficulty
We propose a novel loss function that dynamically re-scales the cross entropy based on prediction difficulty regarding a sample. Deep neural network architectures in image classification tasks struggle to disambiguate visually similar objects. Likewise, in human pose estimation symmetric body parts often confuse the network with assigning indiscriminative scores to them. This is due to the output prediction, in which only the highest confidence label is selected without taking into consideration a measure of uncertainty. In this work, we define the prediction difficulty as a relative property coming from the confidence score gap between positive and negative labels. More precisely, the proposed loss function penalizes the network to avoid the score of a false prediction being significant. To demonstrate the efficacy of our loss function, we evaluate it on two different domains: image classification and human pose estimation. We find improvements in both applications by achieving higher accuracy compared to the baseline methods
Automated Optical Inspection and Image Analysis of Superconducting Radio-Frequency Cavities
The inner surface of superconducting cavities plays a crucial role to achieve
highest accelerating fields and low losses. For an investigation of this inner
surface of more than 100 cavities within the cavity fabrication for the
European XFEL and the ILC HiGrade Research Project, an optical inspection robot
OBACHT was constructed. To analyze up to 2325 images per cavity, an image
processing and analysis code was developed and new variables to describe the
cavity surface were obtained. The accuracy of this code is up to 97% and the
PPV 99% within the resolution of 15.63 . The optical obtained
surface roughness is in agreement with standard profilometric methods. The
image analysis algorithm identified and quantified vendor specific fabrication
properties as the electron beam welding speed and the different surface
roughness due to the different chemical treatments. In addition, a correlation
of with a significance of between an obtained
surface variable and the maximal accelerating field was found
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