Improving phenotypic measurements in high-content imaging screens, p.2017 ,

Combogan: Unrestrained scalability for image domain translation, 2017. ,

Testing that distributions are close, 41st Annual Symposium on Foundations of Computer Science, FOCS 2000, pp.259-269, 2000. ,

Domain adaptation for microscopy imaging, IEEE Trans Med Imaging, vol.34, issue.5, pp.1125-1139, 2015. ,

Analysis of representations for domain adaptation, Proceedings of the 19th International Conference on Neural Information Processing Systems, NIPS'06, pp.137-144, 2006. ,

A theory of learning from different domains, Machine Learning, vol.79, pp.151-175, 2010. ,

Scalable unsupervised domain adaptation for electron microscopy, Medical Image Computing and Computer-Assisted Intervention-MICCAI 2016, pp.326-334, 2016. ,

Discriminative learning for differing training and test distributions, Proceedings of the 24th International Conference on Machine Learning, ICML '07, pp.81-88, 2007. ,

Statistical methods for analysis of high-throughput RNA interference screens, Nat. Methods, vol.6, issue.8, pp.569-575, 2009. ,

Integrating structured biological data by Kernel Maximum Mean Discrepancy, Bioinformatics, vol.22, issue.14, pp.49-57, 2006. ,

Domain separation networks. CoRR, 2016. ,

Stability and generalization, Journal of Machine Learning Research, vol.2, pp.499-526, 2002. ,

High-content phenotypic profiling of drug response signatures across distinct cancer cells, Mol. Cancer Ther, vol.9, issue.6, pp.1913-1926, 2010. ,

Partial transfer learning with selective adversarial networks, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. ,

A method for traffic sign detection in an image with learning from synthetic data, 14th International Conference Digital Signal Processing and its Applications, vol.2, pp.316-319, 2012. ,

Stargan: Unified generative adversarial networks for multi-domain image-to-image translation, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. ,

Torch7: A matlab-like environment for machine learning, BigLearn, NIPS Workshop, 2011. ,

Optimal transport for domain adaptation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, issue.9, pp.1853-1865, 2017. ,

URL : https://hal.archives-ouvertes.fr/hal-01170705

Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation, Devis Tuia, and Nicolas Courty, 2018. ,

Domain adaptation for statistical classifiers, J. Artif. Intell. Res, vol.26, pp.101-126, 2006. ,

Multi-domain learning by confidence-weighted parameter combination, Machine Learning, vol.79, pp.123-149, 2010. ,

Joint cross-domain classification and subspace learning for unsupervised adaptation, Pattern Recognition Letters, vol.65, pp.60-66, 2015. ,

Domain-adversarial training of neural networks, Journal of Machine Learning Research, vol.17, issue.59, pp.1-35, 2016. ,

URL : https://hal.archives-ouvertes.fr/hal-01624607

Deep reconstructionclassification networks for unsupervised domain adaptation, Computer Vision-ECCV, pp.597-613, 2016. ,

Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation, Proceedings of the 30th International Conference on International Conference on Machine Learning, vol.28, 2013. ,

A kernel method for the two-sample-problem, NIPS, pp.513-520, 2007. ,

Correcting sample selection bias by unlabeled data, Proceedings of the 19th International Conference on Neural Information Processing Systems, NIPS'06, pp.601-608, 2006. ,

Image-to-image translation with conditional adversarial networks, CVPR, pp.5967-5976, 2017. ,

, Convolutional architecture for fast feature embedding, 2014.

SciPy: Open source scientific tools for Python, 2001. ,

Unsupervised domain adaptation in brain lesion segmentation with adversarial networks, Information Processing in Medical Imaging, pp.597-609, 2017. ,

Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines, Nat. Biotechnol, vol.34, issue.1, pp.70-77, 2016. ,

Detecting change in data streams, Proceedings of the Thirtieth International Conference on Very Large Data Bases, vol.30, pp.180-191, 2004. ,

Domain adaptation by mixture of alignments of second-or higherorder scatter tensors, 2016. ,

Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, vol.25, pp.1097-1105, 2012. ,

Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, pp.2278-2324, 1998. ,

Unsupervised image-to-image translation networks, Advances in Neural Information Processing Systems, vol.30, pp.700-708, 2017. ,

Annotated high-throughput microscopy image sets for validation, Nat. Methods, vol.9, issue.7, p.637, 2012. ,

Learning transferable features with deep adaptation networks, Proceedings of the 32Nd International Conference on International Conference on Machine Learning, vol.37, pp.97-105, 2015. ,

Deep transfer learning with joint adaptation networks. CoRR, abs/1605.06636, 2016. ,

Domain adaptation with randomized multilinear adversarial networks. CoRR, abs/1705.10667, 2017. ,

Learning and domain adaptation, Algorithmic Learning Theory, 20th International Conference, ALT, pp.4-6, 2009. ,

Domain adaptation with multiple sources, Proceedings of the 21st International Conference on Neural Information Processing Systems, NIPS'08, pp.1041-1048, 2008. ,

Unified deep supervised domain adaptation and generalization, The IEEE International Conference on Computer Vision (ICCV), 2017. ,

Domain generalization via invariant feature representation, Proceedings of the 30th International Conference on International Conference on Machine Learning, vol.28, 2013. ,

A Threshold Selection Method from Gray-level Histograms, IEEE Transactions on Systems, Man and Cybernetics, vol.9, issue.1, pp.62-66, 1979. ,

A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.10, pp.1345-1359, 2010. ,

Multi-adversarial domain adaptation, Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 2018. ,

Direct transfer of learned information among neural networks, Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), AAAI'91, pp.584-589, 1991. ,

Globally optimal stitching of tiled 3D microscopic image acquisitions, Bioinformatics, vol.25, issue.11, pp.1463-1465, 2009. ,

ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision (IJCV), vol.115, issue.3, pp.211-252, 2015. ,

Adapting visual category models to new domains, Computer Vision-ECCV 2010, pp.213-226, 2010. ,

Generate to adapt: Aligning domains using generative adversarial networks, 2017. ,

NIH Image to ImageJ: 25 years of image analysis, Nat. Methods, vol.9, issue.7, pp.671-675, 2012. ,

An empirical analysis of domain adaptation algorithms for genomic sequence analysis, Proceedings of the 21st International Conference on Neural Information Processing Systems, NIPS'08, pp.1433-1440, 2008. ,

Improving predictive inference under covariate shift by weighting the log-likelihood function, Journal of Statistical Planning and Inference, vol.90, issue.2, pp.227-244, 2000. ,

A DIRT-T approach to unsupervised domain adaptation, Proceedings of the 6th International Conference on Learning Representations (ICLR), 2018. ,

Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins, Nat. Methods, vol.3, issue.7, pp.525-531, 2006. ,

Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556, 2014. ,

Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition, Neural Networks, 2012. ,

Computer vision for image-based transcriptomics, Methods, vol.85, pp.44-53, 2015. ,

Deep coral: Correlation alignment for deep domain adaptation, Computer Vision-ECCV 2016 Workshops, pp.443-450, 2016. ,

Return of frustratingly easy domain adaptation, Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI, 2016. ,

Unsupervised cross-domain image generation, 2016. ,

Simultaneous deep transfer across domains and tasks, 2015 IEEE International Conference on Computer Vision (ICCV), pp.4068-4076, 2015. ,

Deep domain confusion: Maximizing for domain invariance. CoRR, abs/1412, vol.3474, 2014. ,

Adversarial discriminative domain adaptation. CoRR, abs/1702.05464, 2017. ,

Leveraging heterogeneity across multiple data sets increases accuracy of cell-mixture deconvolution and reduces biological and technical biases. bioRxiv, 2017. ,

Visualizing data using t-SNE, Journal of Machine Learning Research, vol.9, pp.2579-2605, 2008. ,

Transfer learning improves supervised image segmentation across imaging protocols, I E E E Transactions on Medical Imaging, vol.34, issue.5, pp.1018-1030, 2015. ,

, Statistical Learning Theory, 1998.

Learning deep feature representations with domain guided dropout for person re-identification, Proceedings of the **th Conference on Computer Vision and Pattern Recognition, CVPR'16, 2016. ,

A Survey of Transfer and Multitask Learning in Bioinformatics, Journal of Computing Science and Engineering, 2011. ,

A unified perspective on multi-domain and multi-task learning, Proceedings of the 3d International Conference on Representation Learning, ICLR'15, 2015. ,

Dualgan: Unsupervised dual learning for image-toimage translation, ICCV, pp.2868-2876, 2017. ,

Generalization bounds for domain adaptation, Advances in Neural Information Processing Systems, vol.25, pp.3320-3328, 2012. ,

Importance weighted adversarial nets for partial domain adaptation, 2018. ,

Unpaired image-to-image translation using cycle-consistent adversarial networks, ICCV, pp.2242-2251, 2017. ,