2 research outputs found
A Spacecraft Dataset for Detection, Segmentation and Parts Recognition
Virtually all aspects of modern life depend on space technology. Thanks to
the great advancement of computer vision in general and deep learning-based
techniques in particular, over the decades, the world witnessed the growing use
of deep learning in solving problems for space applications, such as
self-driving robot, tracers, insect-like robot on cosmos and health monitoring
of spacecraft. These are just some prominent examples that has advanced space
industry with the help of deep learning. However, the success of deep learning
models requires a lot of training data in order to have decent performance,
while on the other hand, there are very limited amount of publicly available
space datasets for the training of deep learning models. Currently, there is no
public datasets for space-based object detection or instance segmentation,
partly because manually annotating object segmentation masks is very time
consuming as they require pixel-level labelling, not to mention the challenge
of obtaining images from space. In this paper, we aim to fill this gap by
releasing a dataset for spacecraft detection, instance segmentation and part
recognition. The main contribution of this work is the development of the
dataset using images of space stations and satellites, with rich annotations
including bounding boxes of spacecrafts and masks to the level of object parts,
which are obtained with a mixture of automatic processes and manual efforts. We
also provide evaluations with state-of-the-art methods in object detection and
instance segmentation as a benchmark for the dataset. The link for downloading
the proposed dataset can be found on
https://github.com/Yurushia1998/SatelliteDataset
Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection
We present a method to compute the Shapley values of reconstruction errors of
principal component analysis (PCA), which is particularly useful in explaining
the results of anomaly detection based on PCA. Because features are usually
correlated when PCA-based anomaly detection is applied, care must be taken in
computing a value function for the Shapley values. We utilize the probabilistic
view of PCA, particularly its conditional distribution, to exactly compute a
value function for the Shapely values. We also present numerical examples,
which imply that the Shapley values are advantageous for explaining detected
anomalies than raw reconstruction errors of each feature.Comment: Workshop on Learning and Mining with Industrial Data (LMID) 2019.
typos fixe