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Found 7 papers, 2 papers with code
Feature Accentuation: Revealing 'What' Features Respond to in Natural Images
Most research has primarily centered around attribution methods, which provide explanations in the form of heatmaps, showing where the model directs its attention for a given feature.
How does the primate brain combine generative and discriminative computations in vision?
The alternative conception is that of vision as an inference process in Helmholtz's sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes giving rise to it.
Getting aligned on representational alignment
Finally, we lay out open problems in representational alignment where progress can benefit all three of these fields.
The neuroconnectionist research programme
Artificial Neural Networks (ANNs) inspired by biology are beginning to be widely used to model behavioral and neural data, an approach we call neuroconnectionism.
Pruning for Feature-Preserving Circuits in CNNs
Deep convolutional neural networks are a powerful model class for a range of computer vision problems, but it is difficult to interpret the image filtering process they implement, given their sheer size.
What can 5.17 billion regression fits tell us about artificial models of the human visual system?
Rapid simultaneous advances in machine vision and cognitive neuroimaging present an unparalleled opportunity to assess the current state of artificial models of the human visual system.
Emergent Properties of Foveated Perceptual Systems
The primary model has a foveated-textural input stage, which we compare to a model with foveated-blurred input and a model with spatially-uniform blurred input (both matched for perceptual compression), and a final reference model with minimal input-based compression.
