The attentive reconstruction of objects facilitates robust object recognition
Fig 3
Step-wise visualizations of reconstruction-based feature binding.
The matrices show binding coefficients between object slots (rows) and feature slots (columns) for three time steps. The binding coefficients were initialized to one at t = 1, resulting in the dark matrix on the top, but in the subsequent time steps there is a rapid selective suppression of coefficients (matrix cells becoming lighter) as the model learns the object features leading to higher reconstruction accuracy. In the illustrated example, the coefficient matrix becomes sparser with each iteration as ORA focuses its attention on the features of the digit 3 (darker line forming along the fourth row). The middle column of bar graphs show the hypothesis yielding the highest reconstruction score (in blue), and the rightmost column shows the class likelihood adjusted by the reconstruction score. For clarity, only 5 object slots and 20 feature slots are illustrated, but the full figure can be found in S1 Fig.