1. Babbar, R., Partalas, I., Gaussier, E., Amini, M.R., Amblard, C.: Learning taxonomy adaptation in large-scale classification. J. Mach. Learn. Res. 17(98), 1–37 (2016)
2. Bertinetto, L., Mueller, R., Tertikas, K., Samangooei, S., Lord, N.A.: Making better mistakes: leveraging class hierarchies with deep networks. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12506–12515 (2020)
3. Blockeel, H., Bruynooghe, M., Džeroski, S., Ramon, J., Struyf, J.: Hierarchical multi-classification. In: Workshop Notes of the KDD’02 Workshop on Multi-relational Data Mining, pp. 21–35 (2002)
4. Chang, D., Pang, K., Zheng, Y., Ma, Z., Song, Y.Z., Guo, J.: Your “flamingo” is my “bird”: fine-grained, or not. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11476–11485 (2021)
5. Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the CIFAR datasets. arXiv preprint abs/1707.08819 (2017)