2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CV2024
Understanding Inhibition Through Maximally Tense Images
Chris Hamblin, Srijani Saha, Talia Konkle +1
We address the functional role of 'feature inhibition' in vision models; that is, what are the mechanisms by which a neural network ensures images do not express a given feature? W…
cs.CV2024
Feature Accentuation: Revealing 'What' Features Respond to in Natural Images
Chris Hamblin, Thomas Fel, Srijani Saha +2
Efforts to decode neural network vision models necessitate a comprehensive grasp of both the spatial and semantic facets governing feature responses within images. Most research ha…
cs.CV2022★ 2 cited
Pruning for Feature-Preserving Circuits in CNNs
Chris Hamblin, Talia Konkle, George Alvarez
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,…