Latent DIRE

Computing DIRE features in latent space.

Pipeline

1. Image (either real image or generated from a Generative Model, ADM or GAN)
2. Encode
3. Invert Image and Reconstruct it
4. Compute DIRE with the reconstructed-latent image or then with the decoder image

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Project Organization

├── LICENSE
├── Makefile
├── README.md
├── data
│   └── raw            <- The original, immutable data dump.
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-xy-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- Requirements file for reproducing the environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- Makes project pip installable (pip install -e .) so src can be imported
└── src                <- Source code for use in this project.
    ├── __init__.py    <- Makes src a Python module
    └── data           <- Scripts to download or generate data