107,526 research outputs found
Detection of out-of-distribution samples using binary neuron activation patterns
Deep neural networks (DNN) have outstanding performance in various
applications. Despite numerous efforts of the research community,
out-of-distribution (OOD) samples remain a significant limitation of DNN
classifiers. The ability to identify previously unseen inputs as novel is
crucial in safety-critical applications such as self-driving cars, unmanned
aerial vehicles, and robots. Existing approaches to detect OOD samples treat a
DNN as a black box and evaluate the confidence score of the output predictions.
Unfortunately, this method frequently fails, because DNNs are not trained to
reduce their confidence for OOD inputs. In this work, we introduce a novel
method for OOD detection. Our method is motivated by theoretical analysis of
neuron activation patterns (NAP) in ReLU-based architectures. The proposed
method does not introduce a high computational overhead due to the binary
representation of the activation patterns extracted from convolutional layers.
The extensive empirical evaluation proves its high performance on various DNN
architectures and seven image datasets
What is Holding Back Convnets for Detection?
Convolutional neural networks have recently shown excellent results in
general object detection and many other tasks. Albeit very effective, they
involve many user-defined design choices. In this paper we want to better
understand these choices by inspecting two key aspects "what did the network
learn?", and "what can the network learn?". We exploit new annotations
(Pascal3D+), to enable a new empirical analysis of the R-CNN detector. Despite
common belief, our results indicate that existing state-of-the-art convnet
architectures are not invariant to various appearance factors. In fact, all
considered networks have similar weak points which cannot be mitigated by
simply increasing the training data (architectural changes are needed). We show
that overall performance can improve when using image renderings for data
augmentation. We report the best known results on the Pascal3D+ detection and
view-point estimation tasks
Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber
We present several studies of convolutional neural networks applied to data
coming from the MicroBooNE detector, a liquid argon time projection chamber
(LArTPC). The algorithms studied include the classification of single particle
images, the localization of single particle and neutrino interactions in an
image, and the detection of a simulated neutrino event overlaid with cosmic ray
backgrounds taken from real detector data. These studies demonstrate the
potential of convolutional neural networks for particle identification or event
detection on simulated neutrino interactions. We also address technical issues
that arise when applying this technique to data from a large LArTPC at or near
ground level
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