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Found 4 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.
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.
Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex
Using our benchmark as an atlas, we offer preliminary answers to overarching questions about levels of analysis (e. g. do models that better predict the representations of individual neurons also predict representational similarity across neural populations?
Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model
Multiple object tracking is a task commonly used to investigate the architecture of human visual attention.
