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README.md

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# :framed_picture: :interrobang: Distortion Generator
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Neural network for creating distortion while keeping embeddings as close as possible. Part of tbe
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research paper under the supervision of Prof. Alexander Kuznetsov.
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Neural network for creating distortion while keeping embeddings as close as possible. Part of the
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research paper _"Unrecognizable Yet Identifiable: Image Distortion with Preserved Embeddings."_
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The code is written in [_TensorFlow v2.12_](https://www.tensorflow.org/).
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![Example Generations](images/meta/example_generations.png)
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The project is structured as follows:
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| File/Folder | Description |
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| ----------- | ----------- |
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| [`embedding.ipynb`](embedding.ipynb) | Notebook for creating the embedding neural network using Triplet Loss function |
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| [`generator.ipynb`](generator.ipynb) | Notebook for training the generator neural network — the central model of our research |
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| [`evaluation.ipynb`](evaluation.ipynb) | Notebook for estimating accuracy of proposed models |
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| [`models.ipynb`](models) | Folder containing trained models |
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| [`images`](images) | Folder containing example images after applying the generator model |
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| [`cli.py`](cli.py) | CLI for running training or evaluation |
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| [`src`](src) | All source files for training and evaluating the models |
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| [`images`](images) | Images with example generations, evaluation plots etc. |
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| [`hyperparams_embedding.json`](hyperparams_embedding.json) | Hyperparameters for training the embedding model |
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| [`hyperparams_generator.json`](hyperparams_generator.json) | Hyperparameters for training the generator model |
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| [`dataset`](dataset) | Dataset which was used for training (actually, the portion of it since we do not want to put everything into the repository) |
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| [`models`](models) | Models' weights after the training. Just so you know, the newest versions of the generator are not included since they weigh too much for GitHub to handle. |

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