Fact or Artifact? Revise Layer-wise Relevance Propagation on various ANN Architectures

Layer-wise relevance propagation (LRP) is a widely used and powerful technique to reveal insights into various artificial neural network (ANN) architectures. LRP is often used in the context of image classification. The aim is to understand, which parts of the input sample have highest relevance and hence most influence on the model prediction. Relevance can be traced back through the network to attribute a certain score to each input pixel. Relevance scores are then combined and displayed as heat maps and give humans an intuitive visual understanding of classification models. Opening the black box to understand the classification engine in great detail is essential for domain experts to gain trust in ANN models. However, there are pitfalls in terms of model-inherent artifacts included in the obtained relevance maps, that can easily be missed. But for a valid interpretation, these artifacts must not be ignored. Here, we apply and revise LRP on various ANN architectures trained as classifiers on geospatial and synthetic data. Depending on the network architecture, we show techniques to control model focus and give guidance to improve the quality of obtained relevance maps to separate facts from artifacts.

It's a long way! Layer-wise Relevance Propagation for Echo State Networks applied to Earth System Variability

Artificial neural networks (ANNs) are known to be powerful methods for many hard problems (e.g. image classification, speech recognition or time series prediction). However, these models tend to produce black-box results and are often difficult to interpret. Layer-wise relevance propagation (LRP) is a widely used technique to understand how ANN models come to their conclusion and to understand what a model has learned. Here, we focus on Echo State Networks (ESNs) as a certain type of recurrent neural networks, also known as reservoir computing. ESNs are easy to train and only require a small number of trainable parameters, but are still black-box models. We show how LRP can be applied to ESNs in order to open the black-box. We also show how ESNs can be used not only for time series prediction but also for image classification: Our ESN model serves as a detector for El Nino Southern Oscillation (ENSO) from sea surface temperature anomalies. ENSO is actually a well-known problem and has been extensively discussed before. But here we use this simple problem to demonstrate how LRP can significantly enhance the explainablility of ESNs.

Automated identification of metamorphic test scenarios for an ocean-modeling application

Metamorphic testing seeks to validate software in the absence of test oracles. Our application domain is ocean modeling, where test oracles often do not exist, but where symmetries of the simulated physical systems are known. In this short paper we present work in progress for automated generation of metamorphic test scenarios using machine learning. Metamorphic testing may be expressed as f(g(X))=h(f(X)) with f being the application under test, with input data X, and with the metamorphic relation (g, h). Automatically generated metamorphic relations can be used for constructing regression tests, and for comparing different versions of the same software application. Here, we restrict to h being the identity map. Then, the task of constructing tests means finding different g which we tackle using machine learning algorithms. These algorithms typically minimize a cost function. As one possible g is already known to be the identity map, for finding a second possible g, we construct the cost function to minimize for g being a metamorphic relation and to penalize for g being the identity map. After identifying the first metamorphic relation, the procedure is repeated with a cost function rewarding g that are orthogonal to previously found metamorphic relations. For experimental evaluation, two implementations of an ocean-modeling application will be subjected to the proposed method with the objective of presenting the use of metamorphic relations to test the implementations of the applications.