Towards Anomaly Explanation in Feature Models

Feature models are a wide-spread approach to variability and commonality management in software product lines. Due to the increasing size and complexity of feature models, anomalies in terms of inconsistencies and redundancies can occur which lead to increased efforts related to feature model development and maintenance. In this paper we introduce knowledge representations which serve as a basis for the explanation of anomalies in feature models. On the basis of these representations we show how explanation algorithms can be applied. The results of a performance analysis show the applicability of these algorithms for anomaly detection in feature models. We conclude the paper with a discussion of future research issues

Towards Visually Explaining Variational Autoencoders

Recent advances in Convolutional Neural Network (CNN) model interpretability have led to impressive progress in visualizing and understanding model predictions. In particular, gradient-based visual attention methods have driven much recent effort in using visual attention maps as a means for visual explanations. A key problem, however, is these methods are designed for classification and categorization tasks, and their extension to explaining generative models, e.g. variational autoencoders (VAE) is not trivial. In this work, we take a step towards bridging this crucial gap, proposing the first technique to visually explain VAEs by means of gradient-based attention. We present methods to generate visual attention from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAE predictions. We show how these attention maps can be used to localize anomalies in images, demonstrating state-of-the-art performance on the MVTec-AD dataset. We also show how they can be infused into model training, helping bootstrap the VAE into learning improved latent space disentanglement, demonstrated on the Dsprites dataset

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

Origins of conductance anomalies in a p-type GaAs quantum point contact

Low temperature transport measurements on a p-GaAs quantum point contact are presented which reveal the presence of a conductance anomaly that is markedly different from the conventional `0.7 anomaly'. A lateral shift by asymmetric gating of the conducting channel is utilized to identify and separate different conductance anomalies of local and generic origins experimentally. While the more generic 0.7 anomaly is not directly affected by changing the gate configuration, a model is proposed which attributes the additional conductance features to a gate-dependent coupling of the propagating states to localized states emerging due to a nearby potential imperfection. Finite bias conductivity measurements reveal the interplay between the two anomalies consistently with a two-impurity Kondo model