Uncovering shared structures in multiclass classification, Proceedings of the 24th international conference on Machine learning, pp.17-24, 2007. ,
A framework for learning predictive structures from multiple tasks and unlabeled data, Journal of Machine Learning Research, vol.6, pp.1817-1853, 2005. ,
Multi-task feature learning, Advances in neural information processing systems, pp.41-48, 2007. ,
Model transfer for object category detection, Proc. 2011 Int. Conf. Computer Vision, pp.2252-2259, 2011. ,
A theory of learning from different domains, Machine learning, vol.79, pp.151-175, 2010. ,
Recognition of planar object classes, Computer Vision and Pattern Recognition, pp.223-230, 1996. ,
Open set domain adaptation, The IEEE International Conference on Computer Vision (ICCV, vol.1, p.3, 2017. ,
Partial transfer learning with selective adversarial networks, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR, 2018. ,
Leave-one-out cross-validation based model selection criteria for weighted ls-svms, Neural Networks, 2006. IJCNN'06. International Joint Conference on, pp.1661-1668, 2006. ,
Marginalized denoising autoencoders for domain adaptation, Proceedings of the 29th International Coference on International Conference on Machine Learning, pp.1627-1634, 2012. ,
, Accelerating learning via knowledge transfer, 2015.
Joint distribution optimal transportation for domain adaptation, Advances in Neural Information Processing Systems, pp.3733-3742, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01620589
Domain adaptation with regularized optimal transport, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp.274-289, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01018698
Optimal transport for domain adaptation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01170705
Sinkhorn distances: Lightspeed computation of optimal transport, Advances in neural information processing systems, pp.2292-2300, 2013. ,
Boosting for transfer learning, Proc. 24th Int. Conf. Machine Learning, pp.193-200, 2007. ,
Robust transfer metric learning for image classification, IEEE Transactions on Image Processing, vol.26, pp.660-670, 2017. ,
Adversarial feature learning, 2016. ,
One-shot learning of object categories, vol.28, pp.594-611, 2006. ,
Unsupervised visual domain adaptation using subspace alignment, IEEE International Conference on Computer Vision, ICCV 2013, pp.2960-2967, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00869417
Object classification from a single example utilizing class relevance metrics, Advances in neural information processing systems, pp.449-456, 2005. ,
Unsupervised domain adaptation by backpropagation, International Conference on Machine Learning, pp.1180-1189, 2015. ,
Domain-adversarial training of neural networks, The Journal of Machine Learning Research, vol.17, pp.2096-2030, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01624607
, A neural algorithm of artistic style, 2015.
Borrowing treasures from the wealthy: Deep transfer learning through selective joint fine-tuning, Proc. IEEE Conference on Computer Vision and Pattern Recognition, vol.6, 2017. ,
Generative adversarial nets, Advances in neural information processing systems, pp.2672-2680, 2014. ,
Optimal kernel choice for large-scale two-sample tests, Advances in neural information processing systems, pp.1205-1213, 2012. ,
Distilling the knowledge in a neural network, 2015. ,
Cross-domain learning methods for high-level visual concept classification, 15th IEEE International Conference on, pp.161-164, 2008. ,
Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012. ,
Stability and hypothesis transfer learning, International Conference on Machine Learning, pp.942-950, 2013. ,
From n to n+1: Multiclass transfer incremental learning, Proc. 2013 IEEE Conf. Computer Vision and Pattern Recognition, pp.3358-3365, 2013. ,
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, Proceedings of the 26th annual international conference on machine learning, pp.609-616, 2009. ,
Unsupervised feature learning for audio classification using convolutional deep belief networks, Advances in neural information processing systems, pp.1096-1104, 2009. ,
Self-taught low-rank coding for visual learning, IEEE Trans. Neural Netw. Learn. Syst, 2017. ,
Regularized adaptation: Theory, algorithms and applications, vol.68, 2007. ,
Coupled generative adversarial networks, Advances in neural information processing systems, pp.469-477, 2016. ,
Transfer feature learning with joint distribution adaptation, 2013 IEEE Int. Conf. Computer Vision, pp.2200-2207, 2013. ,
Unsupervised domain adaptation with residual transfer networks, Adv. Neural Inf. Process Syst, 2016. ,
Deep transfer learning with joint adaptation networks, Proceedings of the 34th International Conference on Machine Learning (International Convention Centre, vol.70, pp.2208-2217, 2017. ,
Learning transferable features with deep adaptation networks, Proc. 32nd Int. Conf. Machine Learning, pp.97-105, 2015. ,
When unsupervised domain adaptation meets tensor representations, The IEEE International Conference on Computer Vision (ICCV, vol.2, 2017. ,
Discriminative transfer learning using similarities and dissimilarities, IEEE Transactions on Neural Networks and Learning Systems, vol.29, issue.7, pp.3097-3110, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-02070137
Discriminative and geometry aware unsupervised domain adaptation, 2017. ,
Class subset selection for transfer learning using submodularity, 2018. ,
Transfer learning via dimensionality reduction, AAAI, vol.8, pp.677-682, 2008. ,
Domain adaptation via transfer component analysis, IEEE Transactions on Neural Networks, vol.22, pp.199-210, 2011. ,
A survey on transfer learning, IEEE Trans. Knowl. Data Eng, vol.22, pp.1345-1359, 2010. ,
Large margin multi-task metric learning, Advances in neural information processing systems, pp.1867-1875, 2010. ,
Mapping estimation for discrete optimal transport, Advances in Neural Information Processing Systems, pp.4197-4205, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01376970
A theoretical analysis of metric hypothesis transfer learning, International Conference on Machine Learning, pp.1708-1717, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01175610
Towards cross-category knowledge propagation for learning visual concepts, Proc. 2011 IEEE Conf. Computer Vision and Pattern Recognition, pp.897-904, 2011. ,
Learning visual representations using images with captions, Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on, pp.1-8, 2007. ,
Self-taught learning: Transfer learning from unlabeled data, Proc. 24th Int. Conf. Machine Learning, 2007. ,
, Hints for thin deep nets, 2014.
ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision (IJCV), vol.115, pp.211-252, 2015. ,
Transfer learning for visual categorization: A survey, IEEE Trans. Neural Netw. Learn. Syst. PP, pp.1-1, 2014. ,
Bregman divergence-based regularization for transfer subspace learning, IEEE Trans. Knowl. Data Eng, vol.22, pp.929-942, 2010. ,
Knowledge transfer with Jacobian matching, Proceedings of the 35th International Conference on Machine Learning, vol.80, pp.4730-4738, 2018. ,
Subspace distribution alignment for unsupervised domain adaptation, BMVC, pp.24-25, 2015. ,
Visual and semantic knowledge transfer for large scale semi-supervised object detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01678123
Is learning the n-th thing any easier than learning the first?, Advances in Neural Information Processing Systems, pp.640-646, 1996. ,
Safety in numbers: Learning categories from few examples with multi model knowledge transfer, Proc. 2010 IEEE Conf. Computer Vision and Pattern Recognition, pp.3081-3088, 2010. ,
Simultaneous deep transfer across domains and tasks, Proceedings of the IEEE International Conference on Computer Vision, pp.4068-4076, 2015. ,
Adversarial discriminative domain adaptation, Computer Vision and Pattern Recognition (CVPR, vol.1, p.4, 2017. ,
Deep domain confusion: Maximizing for domain invariance, 2014. ,
Principles of risk minimization for learning theory, Advances in neural information processing systems, pp.831-838, 1992. ,
Optimal transport: old and new, vol.338, 2008. ,
Extracting and composing robust features with denoising autoencoders, Proceedings of the 25th international conference on Machine learning, pp.1096-1103, 2008. ,
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Journal of Machine Learning Research, vol.11, pp.3371-3408, 2010. ,
Robust and discriminative self-taught learning, International Conference on Machine Learning, pp.298-306, 2013. ,
Cross-domain video concept detection using adaptive svms, Proc. 15th Int. Conf. Multimedia, pp.188-197, 2007. ,
Boosting for transfer learning with multiple sources, Proc. 2010 IEEE Conf. Computer Vision and Pattern Recognition, pp.1855-1862, 2010. ,
A gift from knowledge distillation: Fast optimization, network minimization and transfer learning, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR, 2017. ,
How transferable are features in deep neural networks?, Advances in neural information processing systems, pp.3320-3328, 2014. ,
Attribute-based transfer learning for object categorization with zero/one training example, Computer Vision-ECCV 2010, pp.127-140, 2010. ,
Importance weighted adversarial nets for partial domain adaptation, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR, 2018. ,
Joint geometrical and statistical alignment for visual domain adaptation, 2017. ,
Transfer learning for cross-dataset recognition: a survey, 2017. ,