109,769 research outputs found
Towards better understanding of gradient-based attribution methods for Deep Neural Networks
Understanding the flow of information in Deep Neural Networks (DNNs) is a
challenging problem that has gain increasing attention over the last few years.
While several methods have been proposed to explain network predictions, there
have been only a few attempts to compare them from a theoretical perspective.
What is more, no exhaustive empirical comparison has been performed in the
past. In this work, we analyze four gradient-based attribution methods and
formally prove conditions of equivalence and approximation between them. By
reformulating two of these methods, we construct a unified framework which
enables a direct comparison, as well as an easier implementation. Finally, we
propose a novel evaluation metric, called Sensitivity-n and test the
gradient-based attribution methods alongside with a simple perturbation-based
attribution method on several datasets in the domains of image and text
classification, using various network architectures.Comment: ICLR 201
Pre or Post-Softmax Scores in Gradient-based Attribution Methods, What is Best?
Gradient based attribution methods for neural networks working as classifiers
use gradients of network scores. Here we discuss the practical differences
between using gradients of pre-softmax scores versus post-softmax scores, and
their respective advantages and disadvantages.Comment: 8 pages, 2 figures. A version of this paper has been accepted for
presentation in The IEEE International Conference on Pattern Recognition
Systems (ICPRS-23
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
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