[34] Katherine Hermann, Ting Chen, and Simon Kornblith. The origins and prevalence of texture bias in convolutional neural networks. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (eds.), Advances in Neural Information Processing Systems, volume 33, pp. 19000-19015. Curran Associates, Inc., 2020.
[35] Geoffrey E. Hinton and Sam T. Roweis. Stochastic neighbor embedding. In Suzanne Becker, Sebastian Thrun, and Klaus Obermayer (eds.), Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, NIPS 2002, December 9-14, 2002, Vancouver, British Columbia, Canada], pp. 833-840. MIT Press, 2002.
[36] Shell Xu Hu, Da Li, Jan Stuhmer, Minyoung Kim, and Timothy M. Hospedales. Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9058-9067, 2022. doi: 10.1109/CVPR52688.2022.00886.
[37] Mi-Young Huh, Pulkit Agrawal, and Alexei A. Efros. What makes ImageNet good for transfer learning? CoRR, abs/1608.08614, 2016.
[38] Marius Cătălin Iordan, Michelle R Greene, Diane M Beck, and Li Fei-Fei. Basic level category structure emerges gradually across human ventral visual cortex. Journal of cognitive neuroscience, 27(7):1427-1446, 2015.
[39] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In Marina Meila and Tong Zhang (eds.), Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pp. 4904-4916. PMLR, 18-24 Jul 2021.
[40] Seyed-Mahdi Khaligh-Razavi and Nikolaus Kriegeskorte. Deep supervised, but not unsupervised, models may explain it cortical representation. PLoS computational biology, 10(11): e1003915, 2014.
[41] Marcie L. King, Iris I.A. Groen, Adam Steel, Dwight J. Kravitz, and Chris I. Baker. Similarity judgments and cortical visual responses reflect different properties of object and scene categories in naturalistic images. NeuroImage, 197:368-382, 2019. ISSN 1053-8119. doi: https://doi.org/10.1016/j.neuroimage.2019.04.079.
[42] Dmitry Kobak and George C Linderman. Initialization is critical for preserving global data structure in both t-sne and umap. Nature biotechnology, 39(2):156-157, 2021.
[43] Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey E. Hinton. Similarity of neural network representations revisited. In Kamalika Chaudhuri and Ruslan Salakhutdinov (eds.), Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pp. 3519-3529. PMLR, 2019.
[44] Simon Kornblith, Jonathon Shlens, and Quoc V. Le. Do Better ImageNet Models Transfer Better? In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pp. 2661-2671. Computer Vision Foundation / IEEE, 2019. doi: 10.1109/CVPR.2019.00277.
[45] Simon Kornblith, Ting Chen, Honglak Lee, and Mohammad Norouzi. Why Do Better Loss Functions Lead to Less Transferable Features? In Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (eds.), Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, volume 34, pp. 28648-28662, 2021.
[46] Nikolaus Kriegeskorte, Marieke Mur, and Peter Bandettini. Representational similarity analysis - connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2, 2008. ISSN 1662-5137. doi: 10.3389/neuro.06.004.2008.