Our proposed DropCov can be flexibly integrated with existing deep architectures (e.g., CNNs [ Qinghua Hu is the corresponding author and is with Engineering Research Center of City intelligence and Digital Governance, Ministry of Education of the People's Republic of China. VGG-VD on three small-scale fine-grained datasets) show 0.5 is the best choices of As listed in Table S2, we can see that single L T module brings a little gain for plain GCP . Compared to B-CNN + L T (79.62% training accuracy), plain GCP GCP + L T, while B-CNN + L T achieves significant improvement over B-CNN and plain GCP . On the contrary, the samples involving less redundant information (e.g., scene) have large Such these phenomena show the consistency with our finding. Is second-order information helpful for large-scale visual recognition?

Our proposed DropCov can be flexibly integrated with existing deep architectures (e.g., CNNs [ Qinghua Hu is the corresponding author and is with Engineering Research Center of City intelligence and Digital Governance, Ministry of Education of the People's Republic of China. VGG-VD on three small-scale fine-grained datasets) show 0.5 is the best choices of As listed in Table S2, we can see that single L T module brings a little gain for plain GCP . Compared to B-CNN + L T (79.62% training accuracy), plain GCP GCP + L T, while B-CNN + L T achieves significant improvement over B-CNN and plain GCP . On the contrary, the samples involving less redundant information (e.g., scene) have large Such these phenomena show the consistency with our finding. Is second-order information helpful for large-scale visual recognition?