Fetching the paper…
Reading the bibliography…
The current modus operandi in adapting pre-trained models involves updating all the backbone parameters, ie, full fine-tuning.
Welch, B.L.: The generalization of ‘student’s’problem when several different population varlances are involved. Biometrika 34(1-2), 28–35 (1947)
1947
Earlier work this paper cites.
Wilcoxon, F.: Individual comparisons by ranking methods. In: Breakthroughs in statistics, pp. 196–202. Springer (1992)
1992
Earlier work this paper cites.
LeCun, Y., Huang, F.J., Bottou, L.: Learning methods for generic object recognition with invariance to pose and lighting. In: CVPR (2004)
2004
Earlier work this paper cites.
Li, F.F., Fergus, R., Perona, P.: One-shot learning of object categories. IEEE TPAMI (2006)
2006
Earlier work this paper cites.
Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of machine learning research 9(11) (2008)
2008
Earlier work this paper cites.
Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing. pp. 722–729. IEEE (2008)
2008
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS (2010)
2010
Earlier work this paper cites.
Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: Large-scale scene recognition from abbey to zoo. In: CVPR (2010)
2010
Earlier work this paper cites.
Khosla, A., Jayadevaprakash, N., Yao, B., Fei-Fei, L.: Novel dataset for fine-grained image categorization. In: First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, CO (June 2011)
2011
Earlier work this paper cites.
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011 (2011)
2011
Earlier work this paper cites.
Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-ucsd birds-200-2011 dataset. Tech. Rep. CNS-TR-2011-001, California Institute of Technology (2011)
2011
Earlier work this paper cites.
Parkhi, O.M., Vedaldi, A., Zisserman, A., Jawahar, C.V.: Cats and dogs. In: CVPR (2012)
2012
Earlier work this paper cites.
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: The kitti dataset. International Journal of Robotics Research (2013)
2013
Earlier work this paper cites.
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., , Vedaldi, A.: Describing textures in the wild. In: CVPR (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? NeurIPS 27 (2014)
2014
Earlier work this paper cites.
Kaggle, EyePacs: Kaggle diabetic retinopathy detection (July 2015)
2015
Earlier work this paper cites.
Van Horn, G., Branson, S., Farrell, R., Haber, S., Barry, J., Ipeirotis, P., Perona, P., Belongie, S.: Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection. In: CVPR. pp. 595–604 (2015)
2015
Earlier work this paper cites.
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: ECCV. pp. 69–84. Springer (2016)
2016
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: ECCV. pp. 649–666. Springer (2016)
2016
Earlier work this paper cites.
Cheng, G., Han, J., Lu, X.: Remote sensing image scene classification: Benchmark and state of the art. Proceedings of the IEEE (2017)
2017
Earlier work this paper cites.
Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Fei-Fei, L.: Fine-grained car detection for visual census estimation. In: AAAI (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Lawrence Zitnick, C., Girshick, R.: Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In: CVPR (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Matthey, L., Higgins, I., Hassabis, D., Lerchner, A.: dsprites: Disentanglement testing sprites dataset. https://github.com/deepmind/dsprites-dataset/ (2017)
2017
Earlier work this paper cites.
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in PyTorch. In: NeurIPS Autodiff Workshop (2017)
2017
Cited alongside, same era.
Rebuffi, S.A., Bilen, H., Vedaldi, A.: Learning multiple visual domains with residual adapters. NeurIPS 30 (2017)
2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. NeurIPS 30 (2017)
2017
Cited alongside, same era.
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: ECCV. pp. 801–818 (2018)
2018
Cited alongside, same era.
Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., Van Der Maaten, L.: Exploring the limits of weakly supervised pretraining. In: ECCV (2018)
2021
Later among the works it cites.
Chen*, X., Xie*, S., He, K.: An empirical study of training self-supervised vision transformers. In: ICCV (2021)
2021
Later among the works it cites.
Guo, D., Rush, A., Kim, Y.: Parameter-efficient transfer learning with diff pruning. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). pp. 4884–4896. Association for Computational Linguistics, Online (Aug 2021)
2021
Later among the works it cites.
2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
Rebuffi, S.A., Bilen, H., Vedaldi, A.: Efficient parametrization of multi-domain deep neural networks. In: CVPR. pp. 8119–8127 (2018)
2018
Cited alongside, same era.
Veeling, B.S., Linmans, J., Winkens, J., Cohen, T., Welling, M.: Rotation equivariant cnns for digital pathology. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (2018)
2018
Cited alongside, same era.
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.: GLUE: A multi-task benchmark and analysis platform for natural language understanding. In: Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. pp. 353–355. Association for Computational Linguistics, Brussels, Belgium (Nov 2018). https://doi.org/10.18653/v1/W18-5446,
2018
Cited alongside, same era.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (Jun 2019)
2019
Cited alongside, same era.
Elsayed, G.F., Goodfellow, I., Sohl-Dickstein, J.: Adversarial reprogramming of neural networks. In: ICLR (2019)
2019
Cited alongside, same era.
Girdhar, R., Carreira, J., Doersch, C., Zisserman, A.: Video action transformer network. In: CVPR. pp. 244–253 (2019)
2019
Cited alongside, same era.
Helber, P., Bischke, B., Dengel, A., Borth, D.: Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12(7), 2217–2226 (2019)
2019
Cited alongside, same era.
Later among the works it cites.
Jia, M., Wu, Z., Reiter, A., Cardie, C., Belongie, S., Lim, S.N.: Exploring visual engagement signals for representation learning. In: ICCV (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Lester, B., Al-Rfou, R., Constant, N.: The power of scale for parameter-efficient prompt tuning. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. pp. 3045–3059. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic (Nov 2021)
2021
Later among the works it cites.
Li, X.L., Liang, P.: Prefix-tuning: Optimizing continuous prompts for generation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). pp. 4582–4597. Association for Computational Linguistics, Online (Aug 2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: ICCV (2021)
2021
Later among the works it cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning. pp. 8748–8763. PMLR (2021)
2021
Later among the works it cites.
Strudel, R., Garcia, R., Laptev, I., Schmid, C.: Segmenter: Transformer for semantic segmentation. In: CVPR. pp. 7262–7272 (2021)
2021
Later among the works it cites.
Wang, H., Zhu, Y., Adam, H., Yuille, A., Chen, L.C.: Max-deeplab: End-to-end panoptic segmentation with mask transformers. In: CVPR. pp. 5463–5474 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P.H., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: CVPR. pp. 6881–6890 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
2022
Closest in time.
Bao, H., Dong, L., Piao, S., Wei, F.: BEit: BERT pre-training of image transformers. In: ICLR (2022)
2022
Closest in time.
Ben Zaken, E., Goldberg, Y., Ravfogel, S.: BitFit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). pp. 1–9. Association for Computational Linguistics, Dublin, Ireland (May 2022). https://doi.org/10.18653/v1/2022.acl-short.1,
2022
Closest in time.
Conder, J., Jefferson, J., Jawed, K., Nejati, A., Sagar, M., et al.: Efficient transfer learning for visual tasks via continuous optimization of prompts. In: International Conference on Image Analysis and Processing. pp. 297–309. Springer (2022)
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
He, J., Zhou, C., Ma, X., Berg-Kirkpatrick, T., Neubig, G.: Towards a unified view of parameter-efficient transfer learning. In: ICLR (2022)
2022
Closest in time.
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: CVPR. pp. 16000–16009 (2022)
2022
Closest in time.
Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. CVPR (2022)
2022
Closest in time.
Sandler, M., Zhmoginov, A., Vladymyrov, M., Jackson, A.: Fine-tuning image transformers using learnable memory. In: CVPR. pp. 12155–12164 (2022)
2022
Closest in time.
Wang, R., Chen, D., Wu, Z., Chen, Y., Dai, X., Liu, M., Jiang, Y.G., Zhou, L., Yuan, L.: Bevt: Bert pretraining of video transformers. In: CVPR. pp. 14733–14743 (2022)
2022
Closest in time.
Wang, Z., Zhang, Z., Lee, C.Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. In: CVPR. pp. 139–149 (2022)
2022
Closest in time.