The attentive reconstruction of objects facilitates robust object recognition
Fig 8
Model performance on the ImageNet-C dataset.
Classification accuracy on the y-axes and level of corruption on the x-axes. ORA often demonstrates a clear advantage over the CNN baseline (ResNet-50), particularly with high levels of noise corruption.