Latent GIG Backup

Research project backup for Latent Gradient Integrated Gradients (Latent GIG) - an explainability method for image classifiers that leverages latent space paths.

Download & Extract

# huggingface_hub 설치 (필요시)
pip install huggingface_hub

# 다운로드
huggingface-cli download leekwoon/latent_gig_backup --repo-type dataset --local-dir ./latent_gig_data

# 무결성 확인 (선택사항)
cd latent_gig_data
md5sum -c checksums.md5

# 파일 합치기 및 압축 해제
cat data.tar.gz.part_* | tar -xzvf -

Directory Structure

latent_gig/
├── cleanig/              # Core implementation modules
│   ├── explainer/        # Explainability algorithms (IG, GIG, AGI, Latent GIG)
│   ├── dataset/          # Dataset utilities (ImageNet, OxfordPet, OxfordFlower)
│   ├── classifier/       # Classifier utilities
│   ├── metric/           # Evaluation metrics (DiffID)
│   ├── mar_vae/          # MAR-VAE implementation
│   └── vae_wrapper.py    # VAE wrapper for different models
├── configs/              # Configuration files
│   ├── dataset/          # Dataset configs
│   ├── ig.yaml           # Integrated Gradients config
│   ├── gig.yaml          # Guided IG config
│   ├── agi.yaml          # Adversarial GI config
│   ├── latent_gig.yaml   # Latent GIG config
│   └── superpixel_gig.yaml
├── notebooks/            # Jupyter notebooks
│   └── analysis/         # Result analysis notebooks
├── results/              # Experiment results
│   ├── benchmark_diffid/ # DiffID benchmark results
│   └── classifier_*/     # Trained classifiers
├── scripts/              # Execution scripts
│   ├── baselines/        # Baseline method scripts
│   ├── run_baselines.sh  # Run all baselines
│   ├── run_latent_gig.sh # Run Latent GIG experiments
│   ├── analyze_results.py # Result analysis tool
│   └── diffid.py         # DiffID evaluation
├── papers/               # Related papers
└── requirements.txt      # Python dependencies

Key Components

Explainers

IG (Integrated Gradients): Standard integrated gradients
GIG (Guided IG): Guided version with adversarial paths
AGI (Adversarial GI): Adversarial gradient integration
Latent GIG: Our method using latent space paths with VAE models

VAE Models Supported

SD1: Stable Diffusion v1 VAE
SD2: Stable Diffusion v2 VAE
KD: Kandinsky VAE
MAR: Masked Auto-Regressive VAE

Datasets

ImageNet (subset)
Oxford-IIIT Pet Dataset
Oxford Flower Dataset

Evaluation Metrics

DiffID (Diffusion-generated Image Detection)
Insertion/Deletion curves
Attribution quality metrics

Parameters for Latent GIG

fraction: Amount of latent to move at each step (0.05, 0.1, 0.2, 0.3)
vae_type: VAE model to use (mar, sd1, sd2, kd)

Citation

This is a research project for developing improved explainability methods for image classifiers using latent space interpolation.