The attentive reconstruction of objects facilitates robust object recognition
Fig 4
Model accuracy and reaction time for specific corruption types from MNIST-C-shape.
A: ORA vs our CNN using each model’s best performing encoder (Resnet-18 and a 2-layer CNN, respectively). B: ORA’s reaction time (RT), estimated as the number of forward processing steps required to reach a confidence threshold. While many digits could be recognized in only one feed-forward pass, feedback mechanisms become useful for addressing specific types of noise, such as fog, or when the identity of the input is notably ambiguous, as demonstrated in S5 Fig. Error bars indicate standard deviations from 5 different model runs. Asterisks (*) indicate statistical significance at a p-value of < 0.05.