Dec 19, 2017 · Title:ComboGAN: Unrestrained Scalability for Image Domain Translation. Authors:Asha Anoosheh, Eirikur Agustsson, Radu Timofte, Luc Van Gool.

We have shown a novel construction of the CycleGAN model as ComboGAN, which solves the θ(n2) scaling issue inherent in current image translation experiments.

As the name ComboGAN suggests, we can combine the encoders and decoders of our trained model like building blocks, taking as input any domain and outputting any ...

To solve this issue of exploding model counts, we introduce a new model, ComboGAN, which decouples the domains and networks from each other. ComboGAN's.

Conference Cover Image. Download · Home · Proceedings · cvprw 2018. ComboGAN: Unrestrained Scalability for Image Domain Translation. 2018, pp. 896-8967,. DOI ...

ComboGAN: Unrestrained Scalability for Image Domain Translation · Figures · Topics · 199 Citations · 12 References · Related Papers ...

ComboGAN: Unrestrained Scalability for Image Domain Translation ; Article #: ; Date of Conference: 18-22 June 2018 ; Date Added to IEEE Xplore: 16 December 2018.

ComboGAN (Anoosheh et al., 2018) consists of encoders, decoders, and discriminators to implement the I2I translation of domains, requiring only a single ...

Recently, ComboGAN [9] and StarGAN [10] are proposed to solve multi-domain imageto-image translation problem. ComboGAN [9] requires m models and StarGAN [10] ...

Dec 21, 2017 · Additionally, in contrast, ComboGAN increases the number of model parameters as more domains are added, thus being able to scale up a large ...