System for Deduplication of Machine Generated Designs from Fashion Catalog

  • Rajdeep H. Banerjee
  • Anoop K. Rajagopal
  • Vikram Garg
  • Sumit Borar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 747)


A crucial step in generating synthetic designs using machine learning algorithms involves filtering out designs based on photographs already present in the catalogue. Fashion photographs on online media are imaged under diverse settings in terms of backgrounds, lighting conditions, ambience, model shoots etc. resulting in varying image distribution across domains. Deduping designs across these distributions require moving image from one domain to another. In this work, we propose an unsupervised domain adaptation method to address the problem of image dedup on an e-commerce platform. We present a deep learning architecture to embed data from two different domains without label information to a common feature space using auto-encoders. Simultaneously an adversarial loss is incorporated to ensure that the learned encoded feature space of these two domains are indistinguishable. We compare our approach with baseline calculated with VGG features and state of art CORAL [19] approach. We show with experiments that features learned with proposed approach generalizes better in terms of retrieval performance and visual similarity.


Dedup Domain adaptation GAN Similarity 


  1. 1.
    Daumé III, H.: Frustratingly easy domain adaptation. arXiv preprint arXiv:0907.1815 (2009)
  2. 2.
    Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009, CVPR 2009, pp. 248–255. IEEE (2009)Google Scholar
  3. 3.
    Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180–1189 (2015)Google Scholar
  4. 4.
    Ghifary, M., Kleijn, W.B., Zhang, M., Balduzzi, D., Li, W.: Deep reconstruction-classification networks for unsupervised domain adaptation. In: European Conference on Computer Vision, pp. 597–613. Springer (2016)Google Scholar
  5. 5.
    Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2066–2073. IEEE (2012)Google Scholar
  6. 6.
    Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  7. 7.
    Gopalan, R., Li, R., Chellappa, R.: Domain adaptation for object recognition: an unsupervised approach. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 999–1006. IEEE (2011)Google Scholar
  8. 8.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  9. 9.
    Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR (2017)Google Scholar
  10. 10.
    Kulis, B., Saenko, K., Darrell, T.: What you saw is not what you get: domain adaptation using asymmetric kernel transforms. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1785–1792. IEEE (2011)Google Scholar
  11. 11.
    Lim, J.J., Salakhutdinov, R.R., Torralba, A.: Transfer learning by borrowing examples for multiclass object detection. In: Advances in Neural Information Processing Systems, pp. 118–126 (2011)Google Scholar
  12. 12.
    Liu, M.-Y., Tuzel, O.: Coupled generative adversarial networks. In: Advances in Neural Information Processing Systems, pp. 469–477 (2016)Google Scholar
  13. 13.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  14. 14.
    Patel, V.M., Gopalan, R., Li, R., Chellappa, R.: Visual domain adaptation: a survey of recent advances. IEEE Signal Process. Mag. 32(3), 53–69 (2015)CrossRefGoogle Scholar
  15. 15.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
  16. 16.
    Rajagopal, A.K., Subramanian, R., Ricci, E., Vieriu, R.L., Lanz, O., Sebe, N., et al.: Exploring transfer learning approaches for head pose classification from multi-view surveillance images. Int. J. Comput. Vision 109(1–2), 146–167 (2014)CrossRefGoogle Scholar
  17. 17.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  18. 18.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  19. 19.
    Sun, B., Feng, J., Saenko, K.: Correlation alignment for unsupervised domain adaptation. arXiv preprint arXiv:1612.01939 (2016)

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Myntra Designs Pvt. Ltd.BangaloreIndia

Personalised recommendations