[70] Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, and Aleksander Madry. From imagenet to image classification: Contextualizing progress on benchmarks. In International Conference on Machine Learning, pages 9625-9635. PMLR, 2020.
[71] Lukas Tuggener, Jürgen Schmidhuber, and Thilo Stadelmann. Is it enough to optimize cnn architectures onImagenet? arXiv preprint arXiv:2103.09108, 2021.
[72] Ross Wightman, Hugo Touvron, and Hervé Jégou. Resnet strikes back: An improved training procedure in timm. CoRR, abs/2110.00476, 2021. URL https://arxiv.org/abs/2110.00476.
[73] Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le. Self-training with noisy student improves imagenet classification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10687-10698, 2020.
[74] Saining Xie, Ross B. Girshick, Piotr Dólar, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 5987-5995. IEEE Computer Society, 2017. doi: 10.1109/CVPR.2017.634. URL https://doi.org/10.1109/CVPR.2017.634.
[75] Guanshuo Xu. 1st place solution summary, 2019. URL https://www.kaggle.com/competitions/aptos2019-blindness-detection/discussion/108065.
[76] H. Yassine, K. Tout, and M. Jaber. Improving Lulc Classification from Satellite Imagery Using Deep Learning - Eurosat Dataset. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43B3:369-376, June 2021. doi: 10.5194/isprs-archives-XLIII-B3-2021-369-2021.
[77] Fisher Yu, Dequan Wang, Evan Shelhamer, and Trevor Darrell. Deep layer aggregation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2403-2412, 2018.
[78] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917, 2022.
[79] Weihao Yu, Chenyang Si, Pan Zhou, Mi Luo, Yichen Zhou, Jiashi Feng, Shuicheng Yan, and Xinchao Wang. Metaformer baselines for vision. arXiv preprint arXiv:2210.13452, 2022.
[80] Amir R Zamir, Alexander Sax, William Shen, Leonidas J Guibas, Jitendra Malik, and Silvio Savarese. Taskonomy: Disentangling task transfer learning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3712-3722, 2018.
[81] Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, et al. The visual task adaptation benchmark. 2019.
[82] Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer. Scaling vision transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12104-12113, 2022.
[83] Kevin Zheng. 39th solution-attention gated resnet18 (single model without cv), 2019. URL https://www.kaggle.com/competitions/human-protein-atlas-image-classification/discussion/77637.
[84] Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang. Random erasing data augmentation. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 13001-13008. AAAI Press, 2020. URL https://ojs.aaai.org/index.php/AAAI/article/view/7000.