SAL-Net: Self-Supervised Attribute Learning for Object Recognition and Segmentation

Figure 4

Classification and segmentation performance w.r.t. the number of shared convolutional layers of the attribute feature coding module. The models are trained by randomly selecting 90% samples from the CUB-2011 training set and using the remaining 10% samples for validation.