Cross-Domain Gated Learning for Domain Generalization (original) (raw)

References

• Amjad, R. A., & Geiger, B. C. (2020). Learning representations for Neural Network-Based Classification using the information bottleneck principle. Transactions on Pattern Analysis and Machine Intelligence, 42, 2225–2239.
  Article Google Scholar
• Balaji, Y., Sankaranarayanan, S., & Chellappa, R. (2018). MetaReg: Towards domain generalization using meta-regularization. In Advances in neural information processing systems.
• Carlucci, F.M., D’Innocente, A., Bucci, S., Caputo, B., Tommasi, T. (2019). Domain generalization by solving Jigsaw Puzzles. In conference on computer vision and pattern recognition.
• Chattopadhyay, A., Sarkar, A., Howlader, P., & Balasubramanian, V.N. (2018). Grad-CAM++: generalized gradient-based visual explanations for Deep Convolutional Networks. In IEEE winter conference on applications of computer vision.
• Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., & Chua, T. (2017). SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning. In IEEE Conference on Computer Vision and Pattern Recognition.
• Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834–848.
  Article Google Scholar
• Choi, M.J., Lim, J.J., Torralba, A., & Willsky, A.S. (2010). Exploiting hierarchical context on a large database of object categories. In IEEE Conference on Computer Vision and Pattern Recognition.
• Devries, T., & Taylor, G.W. (2017). Improved regularization of convolutional neural networks with Cutout. Preprint retrieved from arXiv: 1708.04552
• Dou, Q., de Castro, D.C., Kamnitsas, K., & Glocker, B. (2019). Domain generalization via model-agnostic learning of semantic features. In Advances in Neural Information Processing Systems.
• Du, D., Wang, L., Wang, H., Zhao, K., & Wu, G. (2019). Translate-to-recognize networks for RGB-D scene recognition. In IEEE Conference on Computer Vision and Pattern Recognition.
• Du, Y., Xu, J., Xiong, H., Qiu, Q., Zhen, X., Snoek, CGM., & Shao, L. (2020). Learning to learn with variational information bottleneck for domain generalization. European Conference on Computer Vision. Springer.
• Everingham, M., Gool, L. V., Williams, C. K. I., Winn, J. M., & Zisserman, A. (2010). The Pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 88, 303–338.
  Article Google Scholar
• Fang, C., Xu, Y., Rockmore, D.N. (2013). Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias. In IEEE International Conference on Computer Vision.
• Federici, M., Dutta, A., Forré, P., Kushman, N., & Akata, Z. (2020). Learning Robust Representations via Multi-View Information Bottleneck. Preprint retrieved from arXiv:2002.07017.
• Ganin, Y., & Lempitsky, V.S. (2015). Unsupervised domain adaptation by Backpropagation. In International Conference on Machine Learning.
• Ghiasi, G., Lin, T., Le, Q.V. (2018). DropBlock: A regularization method for convolutional networks. In: Advances in Neural Information Processing Systems.
• Ghifary, M., Kleijn, W.B., Zhang, M., & Balduzzi, D. (2015). Domain generalization for object recognition with multi-task autoencoders. In IEEE International Conference on Computer Vision.
• Girshick, R.B. (2015). Fast R-CNN. In IEEE International Conference on Computer Vision.
• Gong, B., Grauman, K., & Sha, F. (2014). Learning kernels for unsupervised domain adaptation with applications to visual object recognition. International Journal of Computer Vision, 109, 3–27.
  Article MathSciNet Google Scholar
• Gupta, S., Girshick, R., Arbeláez, P., & Malik, J. (2014). Learning rich features from RGB-D images for object detection and segmentation. In European Conference on Computer Vision, Springer, pp. 345–360.
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition.
• He, K., Gkioxari, G., Dollár, P., & Girshick, R. B. (2020). Mask R-CNN. IEEE Transaction on Pattern Analysis and Machine Intelligence, 42(2), 386–397.
  Article Google Scholar
• Huang, Z., Wang, H., Xing, E.P., & Huang, D. (2020). Self-challenging improves cross-domain generalization. In European Conference on Computer Vision.
• Kolchinsky, A., Tracey, B. D., & Kuyk, S. V. (2019). Caveats for information bottleneck in deterministic scenarios. Preprint retrieved from arXiv:1808.07593.
• Krizhevsky, A., Sutskever, I., Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems.
• LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791.
  Article Google Scholar
• Li, F., Fergus, R., & Perona, P. (2004). Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. In IEEE Conference on Computer Vision and Pattern Recognition Workshops.
• Li, H., Pan, S.J., Wang, S., Kot, A.C. (2018). Domain generalization with adversarial feature learning. In IEEE Conference on Computer Vision and Pattern Recognition.
• Li, D., Yang, Y., Song, Y., & Hospedales, T.M. (2017). Deeper, Broader and Artier domain generalization. In International Conference on Computer Vision.
• Li, Y., Yang, Y., Zhou, W., & Hospedales, T. M. (2019). Feature-critic networks for heterogeneous domain generalization. InInternational Conference on Machine Learning (pp. 3915-3924). PMLR
• Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y., Hospedales, T.M. (2019a). Episodic training for domain generalization. In IEEE International Conference on Computer Vision.
• Li, H., Wan, R., Wang, S., & Kot, A. C. (2020). Unsupervised domain adaptation in the wild via disentangling representation learning. International Journal of Computer Vision, 129, 267–283.
  Article MathSciNet Google Scholar
• Long, M., Cao, Y., Wang, J., & Jordan, M.I. (2015b). Learning transferable features with deep adaptation networks. In International Conference on Machine Learning.
• Long, J., Shelhamer, E., & Darrell, T. (2015a). Fully convolutional networks for semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition.
• McIlraith, S.A., & Weinberger, K.Q. (2017). Learning to generalize: Meta-learning for domain generalization. In AAAI Conference on Artificial Intelligence.
• Moreno-Torres, J. G., Raeder, T., Alaíz-Rodríguez, R., Chawla, N. V., & Herrera, F. (2012). A unifying view on dataset shift in classification. Pattern Recognition, 45, 521–530.
  Article Google Scholar
• Motiian, S., Piccirilli, M., Adjeroh, D.A., & Doretto, G. (2017). Unified deep supervised domain adaptation and generalization. In IEEE International Conference on Computer Vision.
• Muandet, K., Balduzzi, D., & Schölkopf, B. (2013). Domain generalization via invariant feature representation. In International Conference on Machine Learning.
• Omeiza, D., Speakman, S., Cintas, C., & Weldemariam, K. (2019). Smooth Grad-CAM++: An enhanced inference level visualization technique for deep convolutional neural network models. Preprint retrieved from arXiv: 1908.01224.
• Park, S., Kwak, N. (2016). Analysis on the Dropout effect in convolutional neural networks. In Asian Conference on Computer Vision.
• Park, S., Park, J., Shin, S., & Moon, I. (2018). Adversarial Dropout for supervised and semi-supervised learning. In AAAI Conference on Artificial Intelligence.
• Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., & Chintala. S. (2019). PyTorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems.
• Peng, X. B., Kanazawa, A., Toyer, S., Abbeel, P., & Levine, S. (2019). Variational discriminator bottleneck: Improving imitation learning, inverse RL, and GANs by constraining information flow. Preprint retrieved from arXiv:1810.00821
• Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008). LabelMe: A database and web-based tool for image annotation. International Journal of Computer Vision, 77, 157–173.
  Article Google Scholar
• Saito, K., Kim, D., Sclaroff, S., Darrell, T., & Saenko, K. (2019). Semi-supervised domain adaptation via minimax entropy. In IEEE International Conference on Computer Vision.
• Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. In IEEE International Conference on Computer Vision.
• Shankar, S., Piratla, V., Chakrabarti, S., Chaudhuri, S., Jyothi, P., & Sarawagi, S. (2018). Generalizing across domains via cross-gradient training. Preprint retrieved from arXiv:1804.10745
• Shwartz-Ziv, R., & Tishby, N. (2017). Opening the Black Box of Deep Neural Networks via information. Preprint retrieved from arXiv:1703.00810.
• Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. Preprint retrieved from arXiv:1409.1556.
• Simonyan, K., & Zisserman, A. (2014a). Two-stream convolutional networks for action recognition in videos. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K.Q. Weinberger (Eds.) NIPS, pp. 568–576.
• Singh, K.K., Lee, Y.J. (2017). Hide-and-Seek: Forcing a network to be meticulous for weakly-supervised object and action localization. In IEEE International Conference on Computer Vision.
• Tishby, N., & Zaslavsky, N. (2015). Deep learning and the information Bottleneck principle. In Information Theory Workshop.
• Tishby, N., Pereira, F.C.N., & Bialek, W. (2000). The information bottleneck method. Preprint retrieved from arXiv:physics/0004057
• Tompson, J., Goroshin, R., Jain, A., LeCun, Y., & Bregler, C. (2015). Efficient object localization using convolutional networks. In IEEE Conference on Computer Vision and Pattern Recognition.
• van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.
  MATH Google Scholar
• Venkateswara, H., Eusebio, J., Chakraborty, S., & Panchanathan, S. (2017). Deep hashing network for unsupervised domain adaptation. In IEEE Conference on Computer Vision and Pattern Recognition.
• Wang, H., Ge, S., Lipton, Z.C., & Xing, E.P. (2019a). Learning robust global representations by penalizing local predictive power. In Advances in Neural Information Processing Systems.
• Wang, H., He, Z., Lipton, Z. C., & Xing, E. P. (2019). Learning robust representations by projecting superficial statistics out. Preprint retrieved from arXiv:1903.06256
• Wang, H., Wang, Z., Du, M., Yang, F., Zhang, Z., Ding, S., Mardziel, P., & Hu, X. (2020a). Score-CAM: Score-weighted visual explanations for convolutional neural networks. In IEEE Conference on Computer Vision and Pattern Recognition, Workshops.
• Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., & Gool, L.V. (2016). Temporal segment networks: Towards good practices for deep action recognition. In European Conference on Computer Vision.
• Wang, S., Yu, L., Li, C., Fu, C., & Heng, P. (2020b). Learning from extrinsic and intrinsic supervisions for domain generalization. In European Conference on Computer Vision.
• Wang, L., Guo, S., Huang, W., Xiong, Y., & Qiao, Y. (2017). Knowledge guided disambiguation for large-scale scene classification with multi-resolution CNNs. IEEE Trans Image Process, 26(4), 2055–2068.
  Article MathSciNet Google Scholar
• You, Q., Jin, H., Wang, Z., Fang, C., & Luo, J. (2016). Image captioning with semantic attention. In IEEE Conference on Computer Vision and Pattern Recognition.
• Yue, X., Zhang, Y., Zhao, S., Sangiovanni-Vincentelli, A.L., Keutzer, K., & Gong, B. (2019). Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data. In IEEE International Conference on Computer Vision.
• Zakharov, S., Kehl, W., & Ilic, S. (2019). DeceptionNet: Network-driven domain randomization. In IEEE International Conference on Computer Vision.
• Zhou, B., Khosla, A., Lapedriza, À., Oliva, A., & Torralba, A. (2016a). Learning deep features for discriminative localization. In Conference on Computer Vision and Pattern Recognition.
• Zhou B, Khosla A, Lapedriza À, Torralba A, Oliva A (2016b) Places: An image database for deep scene understanding. Preprint retrieved from arXiv: 1610.02055
• Zhou K, Yang Y, Hospedales TM, Xiang T (2020) Learning to generate novel domains for domain generalization. In European Conference on Computer Vision

Download references