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Unsupervised learning

Domain Generalization

Domain Adaptation

AAE

  • AAE Makhzani, Alireza, et al. "Adversarial autoencoders." arXiv preprint arXiv:1511.05644 (2015).

GAN

pooling

Weakly Supervised Learning

Semi-supervised

optic disk and optic cup segmentation

segmentation

domain adaptation

[Domain Adaptation for Segmentation]

*CycleGAN Multimodal, shape constraints CVPR2018 ⭐⭐⭐⭐⭐

patch-level reinforesment learning


MIML (multi-instance multi label learning)

  • Deep MIML Feng, Ji, and Zhi-Hua Zhou. "Deep MIML Network." AAAI. 2017.

MIL

  • survey Carbonneau, Marc-André, et al. "Multiple instance learning: A survey of problem characteristics and applications." Pattern Recognition (2017).  another link here ⭐⭐⭐⭐⭐

Instance Space Method

  • EM_DD Zhang, Qi, and Sally A. Goldman. "EM-DD: An improved multiple-instance learning technique." Advances in neural information processing systems. 2002. Implement ⭐⭐⭐⭐
  • MI_SVM Andrews, Stuart, Ioannis Tsochantaridis, and Thomas Hofmann. "Support vector machines for multiple-instance learning." Advances in neural information processing systems. 2003. ⭐⭐⭐⭐
  • MILBoost Zhang, Cha, John C. Platt, and Paul A. Viola. "Multiple instance boosting for object detection." Advances in neural information processing systems. 2006. ⭐⭐⭐⭐

Bag Space Method

  • Diverse Density(DD) Maron, Oded, and Tomás Lozano-Pérez. "A framework for multiple-instance learning." Advances in neural information processing systems. 1998. Implement ⭐⭐⭐⭐

  • citation kNN Wang, Jun, and Jean-Daniel Zucker. "Solving multiple-instance problem: A lazy learning approach." (2000): 1119-1125. Implement

  • MInd Cheplygina, Veronika, David MJ Tax, and Marco Loog. "Multiple instance learning with bag dissimilarities." Pattern Recognition 48.1 (2015): 264-275.

  • CCE Zhou, Zhi-Hua, and Min-Ling Zhang. "Solving multi-instance problems with classifier ensemble based on constructive clustering." Knowledge and Information Systems 11.2 (2007): 155-170. implement

  • MILES Chen, Yixin, Jinbo Bi, and James Ze Wang. "MILES: Multiple-instance learning via embedded instance selection." IEEE Transactions on Pattern Analysis and Machine Intelligence 28.12 (2006): 1931-1947.

  • NSK-SVM Gärtner, Thomas, et al. "Multi-instance kernels." ICML. Vol. 2. 2002.

  • mi-Graph Zhou, Zhi-Hua, Yu-Yin Sun, and Yu-Feng Li. "Multi-instance learning by treating instances as non-iid samples." Proceedings of the 26th annual international conference on machine learning. ACM, 2009. implement

  • BoW-SVM

  • EMD-SVM Rubner, Yossi, Carlo Tomasi, and Leonidas J. Guibas. "The earth mover's distance as a metric for image retrieval." International journal of computer vision 40.2 (2000): 99-121.

Ranking

  • **** Fast bundle algorithm for multiple-instance learning
  • **** Multiple-instance ranking: Learning to rank images for image retrieval

others

  • MIL pooling layer Kraus, Oren Z., Jimmy Lei Ba, and Brendan J. Frey. "Classifying and segmenting microscopy images with deep multiple instance learning." Bioinformatics 32.12 (2016): i52-i59.   ⭐⭐⭐⭐

  • multi-instane neural network Ramon, Jan, and Luc De Raedt. "Multi instance neural networks." Proceedings of the ICML-2000 workshop on attribute-value and relational learning. 2000. ⭐⭐⭐

  • ML-KNN Zhang, Min-Ling, and Zhi-Hua Zhou. "ML-KNN: A lazy learning approach to multi-label learning." Pattern recognition 40.7 (2007): 2038-2048.

MIL in Deep Learning

  • Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition Yan, Zhennan, et al. "Multi-instance deep learning: Discover discriminative local anatomies for bodypart recognition." IEEE transactions on medical imaging 35.5 (2016): 1332-1343.
  • MILCNN Sun, Miao, et al. "Multiple instance learning convolutional neural networks for object recognition." Pattern Recognition (ICPR), 2016 23rd International Conference on. IEEE, 2016.
  • Attention Deep MIL Ilse, Maximilian, Jakub M. Tomczak, and Max Welling. "Attention-based Deep Multiple Instance Learning." arXiv preprint arXiv:1802.04712 (2018). ⭐⭐⭐⭐⭐⭐
  • MINN Wang, Xinggang, et al. "Revisiting multiple instance neural networks." Pattern Recognition 74 (2018): 15-24. ⭐⭐⭐⭐⭐

SEMI-SUPERVISED LEARNING

  • unsupervised loss function Sajjadi, Mehdi, Mehran Javanmardi, and Tolga Tasdizen. "Regularization with stochastic transformations and perturbations for deep semi-supervised learning." Advances in Neural Information Processing Systems. 2016.
  • self-ensembling Laine, Samuli, and Timo Aila. "Temporal ensembling for semi-supervised learning." arXiv preprint arXiv:1610.02242 (2016).

Loss Function

  • loss function based on probability map Jetley, Saumya, Naila Murray, and Eleonora Vig. "End-to-end saliency mapping via probability distribution prediction." Proceedings of Computer Vision and Pattern Recognition 2016 (2016): 5753-5761.
  • L-GM loss for image classification Wan, Weitao, et al. "Rethinking Feature Distribution for Loss Functions in Image Classification." arXiv preprint arXiv:1803.02988 (2018). CVPR 2018 ⭐⭐⭐⭐⭐ implement
  • Crystal Loss(softmax+l_2 norm) Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition. submitted to TPAMI 2018. previous version
  • ring loss for face recognation Zheng, Yutong, Dipan K. Pal, and Marios Savvides. "Ring loss: Convex Feature Normalization for Face Recognition." arXiv preprint arXiv:1803.00130 (2018). CVPR 2018 ⭐⭐⭐⭐⭐ implement
  • center loss Wen, Yandong, et al. "A discriminative feature learning approach for deep face recognition." European Conference on Computer Vision. Springer, Cham, 2016. ⭐⭐⭐⭐⭐

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