A Broader Study of Cross-Domain Few-Shot Learning | SpringerLink
Skip to main content
Springer Nature Link
Log in

A Broader Study of Cross-Domain Few-Shot Learning

  • Conference paper
  • First Online:
Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12372))

Included in the following conference series:

  • 6703 Accesses

Abstract

Recent progress on few-shot learning largely relies on annotated data for meta-learning: base classes sampled from the same domain as the novel classes. However, in many applications, collecting data for meta-learning is infeasible or impossible. This leads to the cross-domain few-shot learning problem, where there is a large shift between base and novel class domains. While investigations of the cross-domain few-shot scenario exist, these works are limited to natural images that still contain a high degree of visual similarity. No work yet exists that examines few-shot learning across different imaging methods seen in real world scenarios, such as aerial and medical imaging. In this paper, we propose the Broader Study of Cross-Domain Few-Shot Learning (BSCD-FSL) benchmark, consisting of image data from a diverse assortment of image acquisition methods. This includes natural images, such as crop disease images, but additionally those that present with an increasing dissimilarity to natural images, such as satellite images, dermatology images, and radiology images. Extensive experiments on the proposed benchmark are performed to evaluate state-of-art meta-learning approaches, transfer learning approaches, and newer methods for cross-domain few-shot learning. The results demonstrate that state-of-art meta-learning methods are surprisingly outperformed by earlier meta-learning approaches, and all meta-learning methods underperform in relation to simple fine-tuning by 12.8% average accuracy. In some cases, meta-learning even underperforms networks with random weights. Performance gains previously observed with methods specialized for cross-domain few-shot learning vanish in this more challenging benchmark. Finally, accuracy of all methods tend to correlate with dataset similarity to natural images, verifying the value of the benchmark to better represent the diversity of data seen in practice and guiding future research. Code for the experiments in this work can be found at https://github.com/IBM/cdfsl-benchmark.

Y. Guo and N. C. Codella—Equal contribution.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 11439
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 14299
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Adamson, A.S., Smith, A.: Machine learning and health care disparities in dermatology. JAMA Dermatol. 154(11), 1247–1248 (2018)

    Article  Google Scholar 

  2. Anonymous, A.: Projective sub-space networks for few-sot learning. In: ICLR 2019 OpenReview. https://openreview.net/pdf?id=rkzfuiA9F7

  3. Bertinetto, L., Henriques, J.F., Torr, P.H., Vedaldi, A.: Meta-learning with differentiable closed-form solvers. arXiv preprint arXiv:1805.08136 (2018)

  4. Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., Erhan, D.: Domain separation networks. In: Advances in Neural Information Processing Systems, pp. 343–351 (2016)

    Google Scholar 

  5. Chen, W.Y., Liu, Y.C., Kira, Z., Wang, Y.C.F., Huang, J.B.: A closer look at few-shot classification. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=HkxLXnAcFQ

  6. Chen, Z., Fu, Y., Zhang, Y., Jiang, Y.G., Xue, X., Sigal, L.: Multi-level semantic feature augmentation for one-shot learning. IEEE Trans. Image Process. 28(9), 4594–4605 (2019). https://doi.org/10.1109/tip.2019.2910052

    Article  MathSciNet  MATH  Google Scholar 

  7. Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3606–3613 (2014)

    Google Scholar 

  8. Codella, N., et al.: Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXiv:1902.03368 (2019)

  9. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  10. Dhillon, G., Chaudhari, P., Ravichandran, A., Soatto, S.: A baseline for few-shot image classification. In: ICLR (2020)

    Google Scholar 

  11. Dong, N., Xing, E.P.: Domain adaption in one-shot learning. In: Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N., Ifrim, G. (eds.) ECML PKDD 2018. LNCS (LNAI), vol. 11051, pp. 573–588. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10925-7_35

    Chapter  Google Scholar 

  12. Dudík, M., Phillips, S.J., Schapire, R.E.: Correcting sample selection bias in maximum entropy density estimation. In: NIPS (2006)

    Google Scholar 

  13. Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 1126–1135. JMLR.org (2017)

    Google Scholar 

  14. Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2096–2030 (2016)

    Google Scholar 

  15. Garcia, V., Bruna, J.: Few-shot learning with graph neural networks, pp. 1–13. arXiv:1711.04043 (2017)

  16. Ge, W., Yu, Y.: Borrowing treasures from the wealthy: deep transfer learning through selective joint fine-tuning. In: CVPR (2017)

    Google Scholar 

  17. Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset (2007)

    Google Scholar 

  18. Guo, Y., Shi, H., Kumar, A., Grauman, K., Rosing, T., Feris, R.: SpotTune: transfer learning through adaptive fine-tuning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4805–4814 (2019)

    Google Scholar 

  19. Hariharan, B., Girshick, R.: Low-shot visual recognition by shrinking and hallucinating features. In: IEEE International Conference on Computer Vision (ICCV) (2017). https://arxiv.org/pdf/1606.02819.pdf

  20. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)

    Google Scholar 

  21. Helber, P., Bischke, B., Dengel, A., Borth, D.: EuroSAT: a novel dataset and deep learning benchmark for land use and land cover classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12(7), 2217–2226 (2019)

    Article  Google Scholar 

  22. Hoffman, J., et al.: CyCADA: cycle-consistent adversarial domain adaptation. arXiv preprint arXiv:1711.03213 (2017)

  23. Kang, G., Jiang, L., Yang, Y., Hauptmann, A.G.: Contrastive adaptation network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4893–4902 (2019)

    Google Scholar 

  24. Kim, J., Kim, T., Kim, S., Yoo, C.D.: Edge-labeling graph neural network for few-shot learning. Technical report

    Google Scholar 

  25. Kinyanjui, N.M., et al.: Estimating skin tone and effects on classification performance in dermatology datasets. In: NeurIPS Fair ML for Health Workshop 2019 (2019)

    Google Scholar 

  26. Koniusz, P., Tas, Y., Zhang, H., Harandi, M., Porikli, F., Zhang, R.: Museum exhibit identification challenge for the supervised domain adaptation and beyond. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 815–833. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01270-0_48

    Chapter  Google Scholar 

  27. Kornblith, S., Shlens, J., Le, Q.V.: Do better ImageNet models transfer better? arXiv preprint arXiv:1805.08974 (2018)

  28. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)

    Google Scholar 

  29. Krizhevsky, A., et al.: Learning multiple layers of features from tiny images. Technical report, Citeseer (2009)

    Google Scholar 

  30. Kumar, A., et al.: Co-regularized alignment for unsupervised domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9345–9356 (2018)

    Google Scholar 

  31. Lake, B., Salakhutdinov, R., Gross, J., Tenenbaum, J.: One shot learning of simple visual concepts. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 33 (2011)

    Google Scholar 

  32. Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B.: Human-level concept learning through probabilistic program induction. Science 350(6266), 1332–1338 (2015)

    Article  MathSciNet  Google Scholar 

  33. Lee, K., Maji, S., Ravichandran, A., Soatto, S.: Meta-learning with differentiable convex optimization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10657–10665 (2019)

    Google Scholar 

  34. Li, F.F., Fergus, R., Perona, P.: One-shot learning of object categories. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 594–611 (2006)

    Article  Google Scholar 

  35. Li, X., et al.: Learning to self-train for semi-supervised few-shot classification. Technical report (2019)

    Google Scholar 

  36. Lim, S., Kim, I., Kim, T., Kim, C., Brain, K., Kim, S.: Fast AutoAugment. Technical report (2019)

    Google Scholar 

  37. Liu, Y., et al.: Learning to propagate labels: transductive propagation network for few-shot learning (2019)

    Google Scholar 

  38. Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 2208–2217. JMLR.org (2017)

    Google Scholar 

  39. Mensink, T., Verbeek, J., Perronnin, F., Csurka, G.: Distance-based image classification: generalizing to new classes at near-zero cost. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2624–2637 (2013)

    Article  Google Scholar 

  40. Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)

    Article  Google Scholar 

  41. Nichol, A., Achiam, J., Schulman, J.: On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999 (2018)

  42. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009)

    Article  Google Scholar 

  43. Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning (2016)

    Google Scholar 

  44. Reed, S., et al.: Few-shot autoregressive density estimation: towards learning to learn distributions. arXiv:1710.10304 (2016). 1–11 (2018)

  45. Ren, M., et al.: Meta-learning for semi-supervised few-shot classification. In: ICLR, March 2018. http://arxiv.org/abs/1803.00676bair.berkeley.edu/blog/2017/07/18/

  46. Ren, M., et al.: Meta-learning for semi-supervised few-shot classification. arXiv preprint arXiv:1803.00676 (2018)

  47. Requeima, J., Gordon, J., Bronskill, J., Nowozin, S., Turner, R.E.: Fast and flexible multi-task classification using conditional neural adaptive processes. In: Advances in Neural Information Processing Systems, pp. 7959–7970 (2019)

    Google Scholar 

  48. Rotemberg, V., Halpern, A., Dusza, S.W., Codella, N.C.F.: The role of public challenges and data sets towards algorithm development, trust, and use in clinical practice. Semin. Cutan. Med. Surg. 38(1), E38–E42 (2019)

    Article  Google Scholar 

  49. Saito, K., Kim, D., Sclaroff, S., Darrell, T., Saenko, K.: Semi-supervised domain adaptation via minimax entropy. In: ICCV (2019). http://arxiv.org/abs/1904.06487

  50. Saito, K., Kim, D., Sclaroff, S., Saenko, K.: Universal domain adaptation through self supervision https://arxiv.org/abs/2002.07953 (2020)

  51. Schwartz, E., Karlinsky, L., Feris, R., Giryes, R., Bronstein, A.M.: Baby steps towards few-shot learning with multiple semantics, pp. 1–11 (2019). http://arxiv.org/abs/1906.01905

  52. Schwartz, E., et al.: Delta-encoder: an effective sample synthesis method for few-shot object recognition. In: Neural Information Processing Systems (NIPS) (2018). https://arxiv.org/pdf/1806.04734.pdf

  53. Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, pp. 4077–4087 (2017)

    Google Scholar 

  54. Sun, B., Feng, J., Saenko, K.: Return of frustratingly easy domain adaptation. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)

    Google Scholar 

  55. Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199–1208 (2018)

    Google Scholar 

  56. Triantafillou, E., et al.: Meta-dataset: a dataset of datasets for learning to learn from few examples. arXiv preprint arXiv:1903.03096 (2019)

  57. Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 (2018)

    Article  Google Scholar 

  58. Tseng, H.Y., Lee, H.Y., Huang, J.B., Yang, M.H.: Cross-domain few-shot classification via learned feature-wise transformation. In: ICLR (2020)

    Google Scholar 

  59. Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7167–7176 (2017)

    Google Scholar 

  60. Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems, pp. 3630–3638 (2016)

    Google Scholar 

  61. Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The Caltech-UCSD birds-200-2011 dataset (2011)

    Google Scholar 

  62. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097–2106 (2017)

    Google Scholar 

  63. Wang, Y.X., Girshick, R., Hebert, M., Hariharan, B.: Low-shot learning from imaginary data. arXiv:1801.05401 (2018)

  64. Welinder, P., et al.: Caltech-UCSD Birds 200. Technical report CNS-TR-2010-001, California Institute of Technology (2010)

    Google Scholar 

  65. Yang, J., Yan, R., Hauptmann, A.G.: Cross-domain video concept detection using adaptive SVMs. In: Proceedings of the 15th ACM International Conference on Multimedia, pp. 188–197 (2007)

    Google Scholar 

  66. Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: NIPS (2014)

    Google Scholar 

  67. Yu, A., Grauman, K.: Semantic Jitter: dense supervision for visual comparisons via synthetic images. In: Proceedings of the IEEE International Conference on Computer Vision, October 2017, pp. 5571–5580 (2017). https://doi.org/10.1109/ICCV.2017.594

  68. Zhang, C., Bengio, S., Singer, Y.: Are all layers created equal? arXiv preprint arXiv:1902.01996 (2019)

  69. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)

    Google Scholar 

Download references

Acknowledgement

This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. FA8750-19-C-1001. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency (DARPA). This work was supported in part by CRISP, one of six centers in JUMP, an SRC program sponsored by DARPA. This work is also supported by NSF CHASE-CI #1730158, NSF FET #1911095, NSF CC* NPEO #1826967.

Author information

Authors and Affiliations

  1. University of California San Diego, San Diego, USA

    Yunhui Guo & Tajana Rosing

  2. IBM Research AI, Cambridge, USA

    Noel C. Codella, Leonid Karlinsky, James V. Codella, John R. Smith & Rogerio Feris

  3. Boston University, Boston, USA

    Kate Saenko

Authors
  1. Yunhui Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Noel C. Codella
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leonid Karlinsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. James V. Codella
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. John R. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kate Saenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tajana Rosing
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Rogerio Feris
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yunhui Guo .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 7988 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Guo, Y. et al. (2020). A Broader Study of Cross-Domain Few-Shot Learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12372. Springer, Cham. https://doi.org/10.1007/978-3-030-58583-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58583-9_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58582-2

  • Online ISBN: 978-3-030-58583-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation