Fetching the paper…
Reading the bibliography…
In this paper, we propose Factorized Adversarial Networks (FAN) to solve unsupervised domain adaptation problems for image classification tasks.
A database for handwritten text recognition research
Hull, J.J.: · 1994
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: · 1998
Earlier work this paper cites.
Caltech-256 object category dataset
Griffin, G., Holub, A., Perona, P.: · 2007
Earlier work this paper cites.
Visualizing data using t-sne
Maaten, L.v.d., Hinton, G.: · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
Earlier work this paper cites.
Covariate shift by kernel mean matching
Gretton, A., Smola, A.J., Huang, J., Schmittfull, M., Borgwardt, K.M., Schölkopf, B.: · 2009
Earlier work this paper cites.
Factorized orthogonal latent spaces
Salzmann, M., Ek, C.H., Urtasun, R., Darrell, T.: · 2010
Earlier work this paper cites.
Factorized latent spaces with structured sparsity
Jia, Y., Salzmann, M., Darrell, T.: · 2010
Earlier work this paper cites.
Deconvolutional networks
Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: · 2010
Earlier work this paper cites.
Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
Earlier work this paper cites.
A kernel two-sample test
Gretton, A., Borgwardt, K.M., Rasch, M.J., Schölkopf, B., Smola, A.: · 2012
Earlier work this paper cites.
Deep neural networks for object detection
Szegedy, C., Toshev, A., Erhan, D.: · 2013
Earlier work this paper cites.
Transfer feature learning with joint distribution adaptation
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: · 2013
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
Cited alongside, same era.
Deep domain confusion: Maximizing for domain invariance
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: · 2014
Cited alongside, same era.
Discovering hidden factors of variation in deep networks
Cheung, B., Livezey, J.A., Bansal, A.K., Olshausen, B.A.: · 2014
Cited alongside, same era.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
Cited alongside, same era.
Domain separation networks
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., Erhan, D.: · 2016
Later among the works it cites.
Deep reconstruction-classification networks for unsupervised domain adaptation
Ghifary, M., Kleijn, W.B., Zhang, M., Balduzzi, D., Li, W.: · 2016
Later among the works it cites.
Beyond sharing weights for deep domain adaptation
Rozantsev, A., Salzmann, M., Fua, P.: · 2016
Later among the works it cites.
Deep coral: Correlation alignment for deep domain adaptation
Sun, B., Saenko, K.: · 2016
Later among the works it cites.
Coupled generative adversarial networks
Liu, M.Y., Tuzel, O.: · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Schmidhuber, J.: · 2015
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
Cited alongside, same era.
Learning transferable features with deep adaptation networks
Long, M., Cao, Y., Wang, J., Jordan, M.: · 2015
Cited alongside, same era.
Simultaneous deep transfer across domains and tasks
Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: · 2015
Cited alongside, same era.
Unsupervised domain adaptation by backpropagation
Ganin, Y., Lempitsky, V.: · 2015
Cited alongside, same era.
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B.: · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., Chintala, S.: · 2015
Cited alongside, same era.
Taigman, Y., Polyak, A., Wolf, L.: · 2016
Later among the works it cites.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: · 2016
Later among the works it cites.
Disentangling factors of variation in deep representation using adversarial training
Mathieu, M.F., Zhao, J.J., Zhao, J., Ramesh, A., Sprechmann, P., LeCun, Y.: · 2016
Later among the works it cites.
Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: · 2016
Later among the works it cites.
Return of frustratingly easy domain adaptation
Sun, B., Feng, J., Saenko, K.: · 2016
Later among the works it cites.
Label efficient learning of transferable representations acrosss domains and tasks
Luo, Z., Zou, Y., Hoffman, J., Fei-Fei, L.F.: · 2017
Later among the works it cites.
Adversarial discriminative domain adaptation
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: · 2017
Later among the works it cites.
Unsupervised pixel-level domain adaptation with generative adversarial networks
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.: · 2017
Later among the works it cites.