Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed

By Christopher J. Anders, Talmaj Marinc, David Neumann, Wojciech Samek, K. Müller, Sebastian Lapuschkin. 2019

20 citations recorded by the source corpus.

Today's machine learning models for computer vision are typically trained on very large (benchmark) data sets with millions of samples. These may, however, contain biases, artifacts, or errors that have gone unnoticed and are exploited by the model. In the worst case, the trained model may become a 'Clever Hans' predictor that does not learn a valid and generalizable strategy to solve the problem it was trained for, but bases its decisions on spurious correlations in the training data. Recently developed techniques allow to explain individual model decisions and thus to gain deeper insights into the model's prediction strategies. In this paper, we contribute by providing a comprehensive analysis framework based on a scalable statistical analysis of attributions from explanation methods for large data corpora, here ImageNet. Based on a recent technique - Spectral Relevance Analysis (SpRAy) - we propose three technical contributions and resulting findings: (a) novel similarity metrics based on Wasserstein for comparing attributions to allow for the first time scale, translational, and rotational invariant comparisons of attributions, (b) a scalable quantification of artifactual and poisoned classes where the ML models under study exhibit Clever Hans behavior, (c) a cleaning procedure that allows to relief data of artifacts and biases in a systematic manner yielding significantly reduced Clever Hans behavior, i.e. we un-Hans the ImageNet data corpus. Using this novel method set, we provide qualitative and quantitative analyses of the biases and artifacts in ImageNet and demonstrate that the usage of these insights can give rise to improved models and functionally cleaned data corpora.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Making deep neural networks right for the right scientific reasons by interacting with their explanations (2020, methodology): “…this data augmentation procedure is model-agnostic, although alternatives indeed exist: (manually) adding a discovered data artifact to samples of other classes [50], contrastive examples [51], feature ranking [52] for SVMs and constraints on the input gradients for diﬀerentiable models [4].”
Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond (2020, methodology): “Furthermore, initial steps have been taken to use XAI beyond validation to arrive at better and more predictive models e.g. [119], [7], [8], [6].”