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I am not aware of any model on the TensorFlow Hub which could do that. I also believe that I haven't read a paper with a suitable architecture yet. Which one
by fxtentacle 6y ago
I am not aware of any model on the TensorFlow Hub which could do that.
I also believe that I haven't read a paper with a suitable architecture yet.
Which one are you referring to?
- jaredtn 6y agoCycleGAN: https://github.com/junyanz/CycleGAN https://github.com/junyanz/CycleGAN
- deleted 6y ago[deleted]
- fxtentacle 6y agoCycleGAN is exactly the one from the Google colab notebook that failed for cats to dogs. It'll produce cats with a dog texture, but with cat anatomy.
- dheera 6y agoTensorFlow Hub is probably outdated, as usual. They're still advertising ResNet50 for goodness sake. Try this: https://github.com/NVLabs/FUNIT https://github.com/NVLabs/FUNIT (2019) Or this: https://github.com/clovaai/stargan-v2 https://github.com/clovaai/stargan-v2 (2020) Either of those should probably work for cats to rabbits.
- fxtentacle 6y agoI disagree. Both of them massively reduce the complexity by working on position-aligned images of purely the head. I would estimate that even a picture of a cat from the side will already be too much for this network to handle. These kind of networks learn their feature space mapping by treating the input data set as continuous. So for them to learn a good mapping from cat to dog, it would need to also see photos of an animal that is half cat half dog. If I had to train this case for work, I guess I'd try to go through baby pictures. Dog -> baby dog -> baby cat -> cat. That might work if baby cats and baby dogs look similar enough.