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I wonder if it’s possible to create an Anagram using the approach of Deep Learning.
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I want to try it with University of Tokyo 1S Information Alpha.
- If I have time, I also want to try Ambigram of sound.
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For example, when generating human faces using GAN, if we feed both the original version and the upside-down version to the Discriminator every time, we can create an anagram (?) of human face photos.
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If we do the same thing with Conditional GAN, is it possible to generate characters?
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@nbaji9: An anagram that combines letters and pictures, like “dear / read”.
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- It would be nice to have a model that can be used to generate this kind of artwork.
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Log 20220606
- I think it will be difficult without changing the data, so let’s start by successfully training DCGAN with MNIST.
- After that, I’ll try using notMNIST, which seems promising.
- DCGAN
- It works well when only rotated.
- When anagrammed, it gets stuck in a bad state.
- I think it’s because it hasn’t generalized well, so I’ll try increasing the Dropout.
- What happens if it’s a simple one like 0, 1, or 2? (blu3mo)
- 0 is successful.
- 1 is successful once out of two attempts.
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- Unlike MNIST, it’s not always tilted to the right, which is evidence that it’s working well (blu3mo).
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- notMNIST + DCGAN
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- 1000 iterations
- It’s okay.
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- 5000 iterations
- Various styles are coming out, so it was worth using nonMNIST.
- What if we do an anagram with this?
- It’s impossible.
- What could be the problem?
- https://gangango.com/2018/11/16/post-322/
- The Discriminator is too smart, and the gradients that the Generator can learn have disappeared.
- That seems to be it (blu3mo).
- Maybe we should make the Discriminator dumb.
- The Discriminator is too smart, and the gradients that the Generator can learn have disappeared.
- https://gangango.com/2018/11/16/post-322/
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Log 20220523
- For now, let’s train a GAN to generate “9” using MNIST.
- 400
- It worked ✅.
- Then, let’s try feeding the output of the generator to the Discriminator with a flipped version.
- We can easily do this using Data Augmentation with RandomFlip.
- Add RandomFlip to the end of the Generator.
- Huh, that’s strange.
- 1500
- Only one-directional “9” is coming out normally…?
- I gave up on making it work with both directions and settled for only half of the “9” being generated with a 50% success rate.
- No, that’s not true.
- Even if that were the case, there should still be a flipped “9” in the output.
- https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomFlip
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During inference time, the output will be identical to input. Call the layer with training=True to flip the input.
- That seems to be it (blu3mo).
- I found another way.
- 3500
- Hmm…w
- I appreciate the effort (blu3mo).
- Let’s try with other numbers.
- 5
- 5
- Let’s try with DCGAN.
- Normal 5:
- Adjustments seem to be necessary.
- Anagram 5:
- It’s completely unsuccessful, haha.
- Normal 5:
- Huh, that’s strange.
- Add RandomFlip to the beginning of the Discriminator.
- This means that the flipped version of “9” will also be recognized as “9”.
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- Well, that’s what happens.- It doesn’t matter which one, just choose one.
- For now, let’s train a GAN to generate “9” using MNIST.
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This won’t work (blu3mo)
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For now, let’s leave DCGAN and use a regular GAN to increase the dataset.
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LRDS https://paperswithcode.com/dataset/letter
- Doesn’t seem suitable.
- This dataset seems to be extracted features of character images, not actual character images.
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#might implement