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Found 6 papers, 4 papers with code
Interactive Analysis of CNN Robustness
While convolutional neural networks (CNNs) have found wide adoption as state-of-the-art models for image-related tasks, their predictions are often highly sensitive to small input perturbations, which the human vision is robust against.
How Well do Feature Visualizations Support Causal Understanding of CNN Activations?
A precise understanding of why units in an artificial network respond to certain stimuli would constitute a big step towards explainable artificial intelligence.
Exemplary natural images explain CNN activations better than synthetic feature visualizations
Using a well-controlled psychophysical paradigm, we compare the informativeness of synthetic images \citep{olah2017feature} with a simple baseline visualization, namely exemplary natural images that also strongly activate a specific feature map.
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization
Even if only a single reference image is given, synthetic images provide less information than natural images ($65\pm5\%$ vs. $73\pm4\%$).
Natural Images are More Informative for Interpreting CNN Activations than State-of-the-Art Synthetic Feature Visualizations
Using a well-controlled psychophysical paradigm, we compare the informativeness of synthetic images by Olah et al. [45] with a simple baseline visualization, namely natural images that also strongly activate a specific feature map.
Five Points to Check when Comparing Visual Perception in Humans and Machines
In the second case study, we highlight the difference between necessary and sufficient mechanisms in visual reasoning tasks.
