Comments (2)
can the entries in the database be weighted somehow, thus assigning them priority (or more simply, a higher probability of being picked)? If so, we could "segment" the original painting in various subpictures with high weight so that they are considered first?
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I believe a good idea is to define batches (like buckets) of images. ...... and say to the model "look for something on the batch 1" if you find something accpetable (within a threshold) stop looking, otherwise go to the images on the batch 2... see what's your error there and compare with batch 3
instead of building an entire database, to make "Tranches" or batches with pictures where it tranch will have precedence over the next one... so our images will be on the top tranch
ideally, the model will be smart enough to select the best image from the database... however.. our fine tuning should have more weight in the model.. as our painting is very very particular and has little resemblance with the other images the authors used.
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Related Issues (17)
- Try learning the Sobol filter picture with Relu NN HOT 1
- Try learning the Escher painting with an additional "outside edge"
- Write code to fill the blank gaps given a trained NN/ML algo
- Try implementing/training the SIREN model HOT 2
- Try using SIREN to create patches of the painting outside its range HOT 1
- Fix the interpolator and generate a new grid
- Try other GAN models and Image GPT
- Try using tahn (or other sigmoids) activation functions for extrapolation HOT 4
- Make a paper with the Grid only
- Define a benchmark - Evaluation Metrics
- Feedback from G-Research
- Learn Curvature + Add SIREN as a layer HOT 2
- Study how to train the Inpainting Model
- Get the generative network to communicate with the grid algo
- Learn the Rotation with T-Splines
- Generate More Training Data
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