A Dependency Capturing Code for Robust Object Representation
Raj, R.; Dahlen, D.; Duyck, K.; Yu, C. R.
Show abstract
Sensory inputs conveying information about the environment are often noisy and incomplete, yet the brain can achieve remarkable consistency in object recognition. This cognitive robustness is thought to be enabled by transforming the varying input patterns into invariant representations of objects, but how this transformation occurs computationally remains unclear. Here we propose that sensory coding should follow a principle of maximal dependence capturing to encode associations among structural components that can uniquely identify objects. We show that a computational framework incorporating dimension expansion and a specific form of sparse coding can capture structures that contain maximum information about specific objects, allow redundancy coding, and enable consistent representation of object identities. Using symbol and face recognition, we demonstrate that a two-layer system can generate representations that remain invariant under conditions of occlusion, corruption, or high noise.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.