Revgan
RevGAN implementation in PyTorch. We extend the Pix2pix and CycleGAN framework by exploring approximately invertible architectures in 2D and 3D. These architectures are approximately invertible by design and thus partially satisfy cycle-consistency before training even begins. Furthermore, since invertible architectures have constant memory complexity in depth, these models can be built arbitrarily deep without requiring additional memory. In the paper we demonstrate superior quantitative output on the Cityscapes and Maps datasets at near constant memory budget.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Gan Compression | 1,063 | 7 months ago | 9 | other | Python | |||||
| [CVPR 2020] GAN Compression: Efficient Architectures for Interactive Conditional GANs | ||||||||||
| Da_detection | 83 | 2 years ago | 19 | mit | Jupyter Notebook | |||||
| Progressive Domain Adaptation for Object Detection | ||||||||||
| Da Retinanet | 58 | 5 months ago | mit | Python | ||||||
| Official Detectron2 implementation of DA-RetinaNet of our Image and Vision Computing 2021 work 'An unsupervised domain adaptation scheme for single-stage artwork recognition in cultural sites' | ||||||||||
| Revgan | 53 | 4 years ago | 6 | other | Python | |||||
| RevGAN implementation in PyTorch. We extend the Pix2pix and CycleGAN framework by exploring approximately invertible architectures in 2D and 3D. These architectures are approximately invertible by design and thus partially satisfy cycle-consistency before training even begins. Furthermore, since invertible architectures have constant memory complexity in depth, these models can be built arbitrarily deep without requiring additional memory. In the paper we demonstrate superior quantitative output on the Cityscapes and Maps datasets at near constant memory budget. | ||||||||||
| Bayesian Cyclegan | 32 | 4 years ago | mit | Python | ||||||
| Bayesian Cycle-Consistent Generative Adversarial Networks via Marginalizing Latent Sampling | ||||||||||
| Learningimage To Imagetranslationusingpairedandunpairedtrainingsamples | 14 | 3 years ago | 3 | other | Python | |||||
| Learning image-to-image translation using paired and unpaired training samples | ||||||||||
| Asgit | 8 | 3 years ago | 1 | mit | Python | |||||
| Pytorch implementation of "Attention-Based Spatial Guidance for Image-to-Image Translation" (WACV 2021) | ||||||||||
| Cyclegan_tensorflow | 5 | 7 years ago | Python | |||||||
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Python
Pytorch
Cityscapes
Cyclegan
Pix2pix