Fetching the paper…
Reading the bibliography…
Today's generative models are capable of synthesizing high-fidelity images, but each model specializes on a specific target domain.
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22
2009
Earlier work this paper cites.
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: A deep convolutional activation feature for generic visual recognition. In: International conference on machine learning. pp. 647–655. PMLR (2014)
2014
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems. vol. 27. Curran Associates, Inc. (2014), https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1717–1724 (2014)
2014
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Proc. ICLR (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: NIPS (2017)
2017
Earlier work this paper cites.
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 30. Curran Associates, Inc. (2017), https://proceedings.neurips.cc/paper/2017/file/8a1d694707eb0fefe65871369074926d-Paper.pdf
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114
2017
Earlier work this paper cites.
Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40
2017
Earlier work this paper cites.
Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision. pp. 2794–2802 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 30. Curran Associates, Inc. (2017), https://proceedings.neurips.cc/paper/2017/file/68053af2923e00204c3ca7c6a3150cf7-Paper.pdf
2017
Earlier work this paper cites.
Zenke, F., Poole, B., Ganguli, S.: Continual learning through synaptic intelligence. In: International Conference on Machine Learning. pp. 3987–3995. PMLR (2017)
2017
Cited alongside, same era.
Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. In: International Conference on Learning Representations (2018)
2018
Cited alongside, same era.
Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for GANs do actually converge? In: International conference on machine learning. pp. 3481–3490. PMLR (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Miyato, T., Koyama, M.: cGANs with projection discriminator. arXiv preprint arXiv:1802.05637 (2018)
Zhai, M., Chen, L., Tung, F., He, J., Nawhal, M., Mori, G.: Lifelong GAN: Continual learning for conditional image generation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2759–2768 (2019)
2019
Later among the works it cites.
Abdal, R., Qin, Y., Wonka, P.: Image2stylegan++: How to edit the embedded images? In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8296–8305 (2020)
2020
Later among the works it cites.
Chen, M., Radford, A., Child, R., Wu, J., Jun, H., Luan, D., Sutskever, I.: Generative pretraining from pixels. In: International Conference on Machine Learning. pp. 1691–1703. PMLR (2020)
2020
Later among the works it cites.
Härkönen, E., Hertzmann, A., Lehtinen, J., Paris, S.: GANSpace: Discovering interpretable GAN controls. Advances in Neural Information Processing Systems 33
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
Shin, H.C., Tenenholtz, N.A., Rogers, J.K., Schwarz, C.G., Senjem, M.L., Gunter, J.L., Andriole, K.P., Michalski, M.: Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In: International workshop on simulation and synthesis in medical imaging. pp. 1–11. Springer (2018)
2018
Cited alongside, same era.
Wang, Y., Wu, C., Herranz, L., van de Weijer, J., Gonzalez-Garcia, A., Raducanu, B.: Transferring GANs: generating images from limited data. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 218–234 (2018)
2018
Cited alongside, same era.
Wu, C., Herranz, L., Liu, X., van de Weijer, J., Raducanu, B., et al.: Memory replay GANs: Learning to generate new categories without forgetting. Advances in Neural Information Processing Systems 31
2018
Cited alongside, same era.
Abdal, R., Qin, Y., Wonka, P.: Image2stylegan: How to embed images into the StyleGAN latent space? In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4432–4441 (2019)
2019
Cited alongside, same era.
Bao, Y., Li, Y., Huang, S.L., Zhang, L., Zheng, L., Zamir, A., Guibas, L.: An information-theoretic approach to transferability in task transfer learning. In: 2019 IEEE International Conference on Image Processing (ICIP). pp. 2309–2313. IEEE (2019)
2019
Cited alongside, same era.
Bau, D., Zhu, J.Y., Wulff, J., Peebles, W., Strobelt, H., Zhou, B., Torralba, A.: Seeing what a GAN cannot generate. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4502–4511 (2019)
2019
Cited alongside, same era.
Geyer, R., Corinzia, L., Wegmayr, V.: Transfer learning by adaptive merging of multiple models. In: International Conference on Medical Imaging with Deep Learning. pp. 185–196. PMLR (2019)
2019
Cited alongside, same era.
2020
Later among the works it cites.
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8110–8119 (2020)
2020
Later among the works it cites.
Li, Y., Zhang, R., Lu, J.C., Shechtman, E.: Few-shot image generation with elastic weight consolidation. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol. 33, pp. 15885–15896. Curran Associates, Inc. (2020), https://proceedings.neurips.cc/paper/2020/file/b6d767d2f8ed5d21a44b0e5886680cb9-Paper.pdf
2020
Later among the works it cites.
2020
Later among the works it cites.
Nguyen, C., Hassner, T., Seeger, M., Archambeau, C.: Leep: A new measure to evaluate transferability of learned representations. In: International Conference on Machine Learning. pp. 7294–7305. PMLR (2020)
2020
Later among the works it cites.
Pidhorskyi, S., Adjeroh, D.A., Doretto, G.: Adversarial latent autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14104–14113 (2020)
2020
Later among the works it cites.
Shen, Y., Gu, J., Tang, X., Zhou, B.: Interpreting the latent space of GANs for semantic face editing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9243–9252 (2020)
2020
Later among the works it cites.
Viazovetskyi, Y., Ivashkin, V., Kashin, E.: StyleGAN2 distillation for feed-forward image manipulation. In: European Conference on Computer Vision. pp. 170–186. Springer (2020)
2020
Later among the works it cites.
Wang, Y., Gonzalez-Garcia, A., Berga, D., Herranz, L., Khan, F.S., Weijer, J.v.d.: Minegan: effective knowledge transfer from GANs to target domains with few images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9332–9341 (2020)
2020
Later among the works it cites.
Yoon, J., Drumright, L.N., Van Der Schaar, M.: Anonymization through data synthesis using generative adversarial networks (ADS-GAN). IEEE journal of biomedical and health informatics 24
2020
Later among the works it cites.
2021
Closest in time.
2021
Closest in time.
Shu, Y., Kou, Z., Cao, Z., Wang, J., Long, M.: Zoo-tuning: Adaptive transfer from a zoo of models. In: International Conference on Machine Learning. pp. 9626–9637. PMLR (2021)
2021
Closest in time.