Set2Model networks: Learning discriminatively to learn generative models

Arandjelovic, 2012, Multiple queries for large scale specific object retrieval, 1 Bergstra, 2010, Theano: a CPU and GPU math compiler in python, 1, 10.25080/Majora-92bf1922-003 Chatfield, 2015, On-the-fly learning for visual search of large-scale image and video datasets, Int. J. Multimed. Inf. Retr., 4, 75, 10.1007/s13735-015-0077-0 Chatfield, K., Zisserman, A., 2012. Visor: towards on-the-fly large-scale object category retrieval. Springer. Asian Conference on Computer Vision, 432–446. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., 2009. Imagenet: a large-scale hierarchical image database. IEEE. Computer Vision and Pattern Recognition, IEEE Conference on, 248–255. Ellis, 1965 Fergus, R., Perona, P., Zisserman, A., 2004. A visual category filter for Google images. Springer. European Conference on Computer Vision, 242–256. Guadarrama, 2014, Open-vocabulary object retrieval, Vol. 2, 6 Holub, 2005, A discriminative framework for modelling object classes, 664 Jain, V., Varma, M., 2011. Learning to re-rank: query-dependent image re-ranking using click data. ACM. Proceedings of the 20th international conference on World wide web, 277–286. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., 2014. Caffe: convolutional architecture for fast feature embedding. ACM. Proceedings of the 22nd ACM International Conference on Multimedia, 675–678. Kingma, D., Ba, J., 2014. Adam: a method for stochastic optimization. arXiv preprintarXiv:14126980. Koch, 2015, Siamese neural networks for one-shot image recognition Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097 Kumar, 2014, Photo recall: using the Internet to label your photos, 771 Lai, K., Bo, L., Fox, D., 2014. Unsupervised feature learning for 3d scene labeling. IEEE. Robotics and Automation (ICRA), IEEE International Conference on, 3050–3057. Lai, K., Bo, L., Ren, X., Fox, D., 2011. A large-scale hierarchical multi-view RGB-D object dataset. IEEE. Robotics and Automation (ICRA), 2011  IEEE International Conference on, 1817–1824. Lai, 2010, Object recognition in 3d point clouds using web data and domain adaptation, I. J. Robotic Res., 29, 1019, 10.1177/0278364910369190 Lake, 2011, One shot learning of simple visual concepts, Vol. 172, 2 Lasserre, J.A., Bishop, C.M., Minka, T.P., 2006. Principled hybrids of generative and discriminative models. IEEE. Computer Vision and Pattern Recognition, IEEE Conference on, volume->1, 87–94. LeCun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791 Mensink, 2013, Distance-based image classification: generalizing to new classes at near-zero cost, IEEE Trans. Pattern Anal. Mach. Intell., 35, 2624, 10.1109/TPAMI.2013.83 Minka, T., 2005. Discriminative models, not discriminative training. Tech. Rep.; Technical Report MSR-TR-2005-144, Microsoft Research. Nilsback, M.E., Zisserman, A., 2008. Automated flower classification over a large number of classes. IEEE. Computer Vision, Graphics & Image Processing, Sixth Indian Conference on, 722–729. Raina, 2004, Classification with hybrid generative/discriminative models, Vol. 16, 545 Russakovsky, 2015, Imagenet large scale visual recognition challenge, Int. J. Comput. Vision (IJCV), 115, 211, 10.1007/s11263-015-0816-y Santoro, 2016, Meta-learning with memory-augmented neural networks, 1842 Schmidhuber, J., Zhao, J., Wiering, M., 1996. Simple principles of metalearning. Istituto Dalle Molle Di Studi Sull Intelligenza Artificiale. Tech. Rep. Schroff, 2011, Harvesting image databases from the web, IEEE Trans. Pattern Anal. Mach. Intell., 33, 754, 10.1109/TPAMI.2010.133 Schwarz, 1978, Estimating the dimension of a model, Ann. Stat., 6, 461, 10.1214/aos/1176344136 Tang, K.D., Tappen, M.F., Sukthankar, R., Lampert, C.H., 2010. Optimizing one-shot recognition with micro-set learning. IEEE. Computer Vision and Pattern Recognition (CVPR), IEEE Conference on, 3027–3034. Tenenbaum, 2011, How to grow a mind: statistics, structure, and abstraction, Science, 331, 1279, 10.1126/science.1192788 Tenorth, 2011, Web-enabled robots, Rob. Autom. Mag., IEEE, 18, 58, 10.1109/MRA.2011.940993 Ustinova, 2016, Learning deep embeddings with histogram loss, 4170 Vinyals, 2016, Matching networks for one shot learning, 3630 Wang, Y.X., Hebert, M., 2016. Learning to learn: model regression networks for easy small sample learning. Springer. European Conference on Computer Vision, 616–634. Yang, L., Hanjalic, A., 2010. Supervised reranking for web image search. ACM. Proceedings of the 18th ACM international conference on Multimedia, 183–192.