Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M. and Rueckert, D., 2019. Self-supervised learning for medical image analysis using image context restoration. Medical image analysis, 58, p.101539.
[paper link][paper_20200211_01]
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T. and Efros, A.A., 2016. Context encoders: Feature learning by inpainting. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2536-2544).
[Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M. and Rueckert, D., 2019. Self-supervised learning for medical image analysis using image context restoration. Medical image analysis, 58, p.101539][paper_20200211_01]
[Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T. and Efros, A.A., 2016. Context encoders: Feature learning by inpainting. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2536-2544)][paper_20200211_02]
#### Presenter:
Fernando Pérez-García
[Link to the slides](https://figshare.com/articles/Self-supervised_learning/11822100)
He, K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
He, K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
Oh, T.H., Jaroensri, R., Kim, C., Elgharib, M., Durand, F.E., Freeman, W.T. and Matusik, W., 2018. Learning-based video motion magnification. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 633-648).
Oh, T.H., Jaroensri, R., Kim, C., Elgharib, M., Durand, F.E., Freeman, W.T. and Matusik, W., 2018. Learning-based video motion magnification. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 633-648).
Jin Y, Dou Q, Chen H, Yu L, Qin J, Fu CW, Heng PA. SV-RCNet: workflow recognition from surgical videos using recurrent convolutional network. IEEE transactions on medical imaging. 2017 Dec 27;37(5):1114-2
Jin Y, Dou Q, Chen H, Yu L, Qin J, Fu CW, Heng PA. SV-RCNet: workflow recognition from surgical videos using recurrent convolutional network. IEEE transactions on medical imaging. 2017 Dec 27;37(5):1114-2
Lasinger, K., Ranftl, R., Schindler, K. and Koltun, V., 2019. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. arXiv preprint arXiv:1907.01341.
Lasinger, K., Ranftl, R., Schindler, K. and Koltun, V., 2019. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. arXiv preprint arXiv:1907.01341.
#### Topic: Spatial Transformation in Deep Neural Networks
#### G03 Seminar Room 1, Charles Bell House
#### Paper(s):
Jaderberg, M., Simonyan, K., & Zisserman, A. (2015). Spatial transformer networks. In Advances in neural information processing systems (pp. 2017-2025).
Jaderberg, M., Simonyan, K., & Zisserman, A. (2015). Spatial transformer networks. In Advances in neural information processing systems (pp. 2017-2025).
Kendall, A., Hawke, J., Janz, D., Mazur, P., Reda, D., Allen, J. M., ... & Shah, A. (2019, May). Learning to drive in a day. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 8248-8254). IEEE.
[paper link][paper_20191119_01]
Kendall, A., Hawke, J., Janz, D., Mazur, P., Reda, D., Allen, J. M., ... & Shah, A. (2019, May). Learning to drive in a day. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 8248-8254). IEEE.
[paper link][paper_20191119_01]
#### Presenter(s):
#### Presenter(s):
Bongjin Koo
...
...
@@ -150,37 +145,37 @@ Bongjin Koo
### -------------------------------
### --- [cancelled] ---
-----------------------------
- [cancelled] ---
#### 12th November 2019, 13:00 - 14:00
#### Topic: Reinforcement Learning
#### G03 Seminar Room 1, Charles Bell House,
#### G03 Seminar Room 1, Charles Bell House,
#### Paper(s):
To be determined
#### Presenter(s):
#### Presenter(s):
Bongjin Koo
### -------------------------------
-----------------------------
#### 22nd October 2019, 13:00 - 14:00
#### Topic: Model Interpretability
#### G03 Seminar Room 1, Charles Bell House,
#### G03 Seminar Room 1, Charles Bell House,
#### Paper(s):
Clough, J.R., Oksuz, I., Puyol-Anton, E., Ruijsink, B., King, A.P. and Schnabel, J.A., 2019. Global and Local Interpretability for Cardiac MRI Classification. arXiv preprint arXiv:1906.06188. MICCAI 2019. [paper link][paper_22102019_01]
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M. and Lempitsky, V., 2016. Domain-adversarial training of neural networks. The Journal of Machine Learning Research, 17(1), pp.2096-2030. [paper link][paper_08102019_00]
...
...
@@ -188,7 +183,7 @@ Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F.
Dou, Q., Ouyang, C., Chen, C., Chen, H. and Heng, P.A., 2018. Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss. arXiv preprint arXiv:1804.10916. [paper link][paper_08102019_01]
