SoftCut: A Fully Differentiable Relaxed Graph Cut Approach for Deep Learning Image Segmentation

Author:

Bonfiglio AlessioORCID,Cannici MarcoORCID,Matteucci MatteoORCID

Publisher

Springer Nature Switzerland

Reference18 articles.

1. Agrawal, A., Boyd, S.: Differentiating through log-log convex programs. arXiv (2020)

2. Amos, B., Kolter, J.Z.: OptNet: differentiable optimization as a layer in neural networks. In: Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 136–145. PMLR (2017)

3. Berthet, Q., Blondel, M., Teboul, O., Cuturi, M., Vert, J.P., Bach, F.: Learning with differentiable pertubed optimizers. In: Advances in Neural Information Processing Systems, vol. 33, pp. 9508–9519 (2020)

4. Lecture Notes in Computer Science;E Borenstein,2002

5. Borse, S., Cai, H., Zhang, Y., Porikli, F.: HS3: learning with proper task complexity in hierarchically supervised semantic segmentation. In: 32nd British Machine Vision Conference 2021, BMVC 2021, Online, 22–25 November 2021, p. 175. BMVA Press (2021)

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