Diverse feature visualizations reveal invariances in early layers of deep neural networks

By Santiago A. Cadena, Marissa A. Weis, Leon A. Gatys, Matthias Bethge, Alexander S. Ecker. 2018

34 citations recorded by the source corpus.

Visualizing features in deep neural networks (DNNs) can help understanding their computations. Many previous studies aimed to visualize the selectivity of individual units by finding meaningful images that maximize their activation. However, comparably little attention has been paid to visualizing to what image transformations units in DNNs are invariant. Here we propose a method to discover invariances in the responses of hidden layer units of deep neural networks. Our approach is based on simultaneously searching for a batch of images that strongly activate a unit while at the same time being as distinct from each other as possible. We find that even early convolutional layers in VGG-19 exhibit various forms of response invariance: near-perfect phase invariance in some units and invariance to local diffeomorphic transformations in others. At the same time, we uncover representational differences with ResNet-50 in its corresponding layers. We conclude that invariance transformations are a major computational component learned by DNNs and we provide a systematic method to study them.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition

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Citations in the corpus

High Fidelity Visualization of What Your Self-Supervised Representation Knows About (2021, neutral): “This led to successful interpretability of DNs internal features, especially when applied on a unit belonging to the first few layers of a DN (Cadena et al., 2018).”
Excessive Invariance Causes Adversarial Vulnerability (2018, background): “The role of invariance has been studied extensively in the context of deep neural networks (Bruna & Mallat, 2013; Mallat, 2016; Achille & Soatto, 2018; Behrmann et al., 2018; Cadena et al., 2018).”
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future (2020, background): “showing which invariances exist at different layers [90].”