7,985 research outputs found
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features
One-class support vector machine (OC-SVM) for a long time has been one of the
most effective anomaly detection methods and extensively adopted in both
research as well as industrial applications. The biggest issue for OC-SVM is
yet the capability to operate with large and high-dimensional datasets due to
optimization complexity. Those problems might be mitigated via dimensionality
reduction techniques such as manifold learning or autoencoder. However,
previous work often treats representation learning and anomaly prediction
separately. In this paper, we propose autoencoder based one-class support
vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier
features to approximate the radial basis kernel, into deep learning context by
combining it with a representation learning architecture and jointly exploit
stochastic gradient descent to obtain end-to-end training. Interestingly, this
also opens up the possible use of gradient-based attribution methods to explain
the decision making for anomaly detection, which has ever been challenging as a
result of the implicit mappings between the input space and the kernel space.
To the best of our knowledge, this is the first work to study the
interpretability of deep learning in anomaly detection. We evaluate our method
on a wide range of unsupervised anomaly detection tasks in which our end-to-end
training architecture achieves a performance significantly better than the
previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201
Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
Convolutional autoencoders have emerged as popular methods for unsupervised
defect segmentation on image data. Most commonly, this task is performed by
thresholding a pixel-wise reconstruction error based on an distance.
This procedure, however, leads to large residuals whenever the reconstruction
encompasses slight localization inaccuracies around edges. It also fails to
reveal defective regions that have been visually altered when intensity values
stay roughly consistent. We show that these problems prevent these approaches
from being applied to complex real-world scenarios and that it cannot be easily
avoided by employing more elaborate architectures such as variational or
feature matching autoencoders. We propose to use a perceptual loss function
based on structural similarity which examines inter-dependencies between local
image regions, taking into account luminance, contrast and structural
information, instead of simply comparing single pixel values. It achieves
significant performance gains on a challenging real-world dataset of
nanofibrous materials and a novel dataset of two woven fabrics over the state
of the art approaches for unsupervised defect segmentation that use pixel-wise
reconstruction error metrics
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