Dissertation on feature construction in vision models

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Image seeded feature visualizations reveal 'what' a feature sees in a given image

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Activation maximization isn't just about visualizing features, it can also tell us about what the network can/can't represent.

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Automated circuit extraction in CNNs

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A tool for Visualizing Interpretable Subgraphs in CNNs

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Feeding an autoencoders outputs as inputs lets you visualize the fixed-points of their embedding space.

A paper testing which model objectives/architectures account for human representational geometry of letter perception. With Daniel Janini

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