[1] I. Alhashim and P. Wonka. High quality monocular depth estimation via transfer learning. arXiv e-prints, abs/1812.11941;arXiv:1812.11941, 2018. URL https://arxiv.org/abs/1812.11941.
[2] S. Azizi, S. Kornblith, C. Sahara, M. Norouzi, and D. J. Fleet. Synthetic data from diffusion models improves imagenet classification. arXiv preprint arXiv:2304.08466, 2023.
[3] D. Baranchuk, I. Rubachev, A. Voynov, V. Khrulkov, and A. Babenko. Label-efficient semantic segmentation with diffusion models. arXiv preprint arXiv:2112.03126, 2021.
[4] A. Barbu, D. Mayo, J. Alverio, W. Luo, C. Wang, D. Gutfreund, J. Tenenbaum, and B. Katz. Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models. Advances in neural information processing systems, 32, 2019.
[5] A. Bartler, A. Bühler, F. Wiewel, M. Döbler, and B. Yang. Mt3: Meta test-time training for self-supervised test-time adaption. In International Conference on Artificial Intelligence and Statistics, pages 3080–3090. PMLR, 2022.
[6] L. Bossard, M. Guillaumin, and L. Van Gool. Food-101 – mining discriminative components with random forests. In European Conference on Computer Vision, 2014.
[7] R. Burgert, K. Ranasinghe, X. Li, and M. S. Ryoo. Peekaboo: Text to image diffusion models are zero-shot segmentors. arXiv preprint arXiv:2211.13224, 2022.
[8] K. Clark and P. Jaini. Text-to-image diffusion models are zero-shot classifiers. arXiv preprint arXiv:2303.15233, 2023.
[9] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009.
[10] J. Donahue, P. Krahenbuhl, and T. Darrell. Adversarial feature learning. arXiv preprint arXiv:1605.09782, 2016.
[11] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
[12] Y. Gandelsman, Y. Sun, X. Chen, and A. Efros. Test-time training with masked autoencoders. Advances in Neural Information Processing Systems, 35:29374-29385, 2022.
[13] R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel. Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations, 2019. URL https://openreview.net/forum?id=Bygh9j09KX.
[14] R. Geirhos, J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F. A. Wichmann. Shortcut learning in deep neural networks. Nature Machine Intelligence, 2(11):665-673, 2020.
[15] C. D. Gilbert and M. Sigman. Brain states: Top-down influences in sensory processing. Neuron, 54(5):677-696, 2007. ISSN 0896-6273. doi: https://doi.org/10.1016/j.neuron.2007.05.019. URL https://www.sciencedirect.com/science/article/pii/S0896627307003765.
[16] J.-B. Grill, F. Strub, F. Alché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33: 21271-21284, 2020.