Are deep neural networks adequate behavioral models of human visual perception?
Deep neural networks (DNNs) are machine learning algorithms that have revolutionized
computer vision due to their remarkable successes in tasks like object classification and …
computer vision due to their remarkable successes in tasks like object classification and …
Deep problems with neural network models of human vision
Deep neural networks (DNNs) have had extraordinary successes in classifying
photographic images of objects and are often described as the best models of biological …
photographic images of objects and are often described as the best models of biological …
Getting aligned on representational alignment
I Sucholutsky, L Muttenthaler, A Weller, A Peng… - ar** in deep neural networks
Gestalt psychologists have identified a range of conditions in which humans organize
elements of a scene into a group or whole, and perceptual grou** principles play an …
elements of a scene into a group or whole, and perceptual grou** principles play an …
Human shape representations are not an emergent property of learning to classify objects.
Humans are particularly sensitive to relationships between parts of objects. It remains
unclear why this is. One hypothesis is that relational features are highly diagnostic of object …
unclear why this is. One hypothesis is that relational features are highly diagnostic of object …
Learning online visual invariances for novel objects via supervised and self-supervised training
Humans can identify objects following various spatial transformations such as scale and
viewpoint. This extends to novel objects, after a single presentation at a single pose …
viewpoint. This extends to novel objects, after a single presentation at a single pose …
Shape-selective processing in deep networks: integrating the evidence on perceptual integration
Understanding how deep neural networks resemble or differ from human vision becomes
increasingly important with their widespread use in Computer Vision and as models in …
increasingly important with their widespread use in Computer Vision and as models in …
Teaching deep networks to see shape: Lessons from a simplified visual world
Deep neural networks have been remarkably successful as models of the primate visual
system. One crucial problem is that they fail to account for the strong shape-dependence of …
system. One crucial problem is that they fail to account for the strong shape-dependence of …