This codebase provides tools for analyzing and visualizing hallucination artifacts in deep learning model predictions. The implementation includes training scripts for multiple architectures (U-Net, SUNet, Learnlets) with different loss functions, evaluation frameworks for hallucination detection, and comprehensive visualization tools.

Note: In this codebase, the metric is referred to as "HIC" or "R", which was later renamed to "CHEM" (Conformal Hallucination Evaluation Metric). Variable names and function references will be updated. If you have questions regarding this work or want to collaborate, feel free to reach out.

├── src/
│   ├── train/           # Training scripts and model definitions
│   ├── eval/            # Evaluation and analysis scripts  
│   ├── models/          # Trained model weights
│   ├── utils/           # Utility functions and data processing
│   └── viz/             # Visualization and plotting tools
├── configs/             # Training and evaluation configurations
├── data/               # Training data
└── results/            # Experimental outputs and figures

Training scripts that monitor and log the CHEM throughout the training process, enabling analysis of hallucination evolution during training.

The test_HIC scripts generate the data used in pyramid plot visualizations, while the class_HIC scripts perform coefficient classification that enables the reconstruction plots presented in the paper.

• FrequencyClassReconstruction.py: Multi-model comparison plots showing predictions and isolated hallucination maps
• fwhm_pyramid_plot.py: Pyramid visualization of CHEM metrics across different conditions

Trained model weights should be placed in the src/models/ directory following this structure:

src/models/
├── LearnLet/
│   ├── L1_Loss/
│   │   └── weights.pth
│   └── L2_Loss/
│       └── weights.pth
├── SUNet/
│   ├── L1_Loss/
│   │   └── weights.pth
│   └── L2_Loss/
│       └── weights.pth
└── U-Net/
    ├── L1_Loss/
    │   └── weights.pth
    └── L2_Loss/
        └── weights.pth

The codebase requires two separate environments depending on the analysis type:

pip install -r requirements_wavelets.txt

pip install -r requirements_shearlets.txt

To reproduce the results presented in the paper:

1. Install the appropriate dependencies for your analysis type
2. Place the trained model weights in src/models/ following the specified structure and the datasets in data/
3. Run the evaluation scripts to generate data
4. Use the visualization tools to create the figures shown in the paper

• PyWavelets: Wavelet transform implementations
• PyTorch: Deep learning framework
• NumPy/SciPy: Numerical computing libraries
• Matplotlib: Visualization library

• Conformalized Quantile Regression (src/utils/cqr.py)
  Adapted from H. Leterme and A. Tersenov, “Weak Lensing Uncertainty Quantification Project,” 2021. https://github.com/hubert-leterme/weaklensing_uq/blob/master/wlmmuq/models/cqr.py . Original methods described in:
  [1] H. Leterme, J. Fadili, and J.-L. Starck, “Distribution-free uncertainty quantification for inverse problems: Application to weak lensing mass mapping,” A&A, vol. 694, p. A267, Feb. 2025.

• SUNet Model, Training Scripts, and Utilities
  Adapted from U. Akhaury, “SUNet Project,” 2022. https://github.com/utsav-akhaury/SUNet/tree/main . Original method described in:
  [2] K. Fan, S. X. Yu, and X. Jin, “SUNet: Swin Transformer UNet for Image Denoising,” arXiv:2202.14009, 2022.
  [3] U. Akhaury, P. Jablonka, J.-L. Starck, & F. Courbin, "Ground-based image deconvolution with Swin Transformer UNet," A&A, 688, A6, 2024.

• Learnlet Transform (src/train/model/LearnLet.py)
  Adapted from V. Bonjean, “Learnlet Repository,” 2025. https://github.com/vicbonj/learnlet/blob/main/learnlet.py . Original methods described in:
  [4] A. Ramzi, J. Fadili, and J.-L. Starck, “Learnlets: Learning Wavelets from Data,” arXiv:2008.10317, 2020.