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README.md

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+# Latent GIG Backup
+
+Research project backup for Manifold-Aware Gradient Integration methods - exploring various path-based explainability methods for image classifiers including latent space paths, spectral decomposition, and manifold-aware approaches.
+
+## Download & Extract
+
+```bash
+# huggingface_hub 설치 (필요시)
+pip install huggingface-hub==0.20.2
+
+# 다운로드
+huggingface-cli download leekwoon/260204_magig_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 (40+ methods)
+│   ├── dataset/          # Dataset utilities (ImageNet, OxfordPet, CIFAR10)
+│   ├── classifier/       # Classifier utilities
+│   ├── metric/           # Evaluation metrics (DiffID, ROAR)
+│   ├── mar_vae/          # MAR-VAE implementation
+│   └── vae_wrapper.py    # VAE wrapper for different models
+├── configs/              # Hydra configuration files (40+ configs)
+├── guided-diffusion/     # Guided diffusion model
+├── models/               # Pre-trained models
+├── notebooks/            # Jupyter notebooks for analysis
+├── results/              # Experiment results
+│   ├── benchmark_diffid/ # DiffID benchmark results
+│   ├── roar/             # ROAR evaluation results
+│   └── classifier_*/     # Trained classifiers
+├── scripts/              # Execution scripts
+│   ├── icml26_*/         # ICML 2026 experiment scripts
+│   ├── diffid.py         # DiffID evaluation
+│   ├── roar.py           # ROAR evaluation
+│   └── measure_computation_time.py
+├── papers/               # Related papers
+└── requirements.txt      # Python dependencies
+```
+
+## Key Components
+
+### Explainers (40+ methods)
+**Baseline Methods:**
+- **IG**: Integrated Gradients (standard linear path)
+- **GIG**: Guided IG (gradient-guided adaptive paths)
+- **AGI**: Adversarial Gradient Integration
+- **BIG**: Blur Integrated Gradients
+- **SAMP**: Adaptive sampling method
+- **XRAI**: Region-based attribution
+
+**Latent Space Methods:**
+- **Latent GIG**: Latent space paths with VAE models
+- **EIG**: Enhanced IG in latent space
+- **MIG**: Manifold IG (geodesic paths on VAE manifold)
+- **Spectral EIG**: Spectral decomposition in latent space
+
+**Spectral Methods:**
+- **Spectral IG**: SVD-based coarse-to-fine path construction
+- **Multiscale Spectral IG**: Multi-resolution spectral paths
+
+**Experimental Methods:**
+- **MCTS GIG**: Monte Carlo Tree Search guided paths
+- **Curvature GIG**: Curvature-aware path construction
+- **Boundary IG**: Decision boundary aware paths
+
+### VAE Models Supported
+- **MAR**: Masked Auto-Regressive VAE
+- **SD1/SD2**: Stable Diffusion VAE (v1, v2)
+- **KD**: Kandinsky VAE
+
+### Datasets
+- ImageNet (ILSVRC2012)
+- Oxford-IIIT Pet Dataset
+- CIFAR-10
+
+### Evaluation Metrics
+- **DiffID**: Insertion/Deletion curves
+- **ROAR**: RemOve And Retrain
+- Computation time benchmarks
+
+## Usage
+
+```bash
+# Install
+pip install -e .
+
+# Run evaluation
+python scripts/diffid.py --config-name latent_gig dataset=imagenet
+python scripts/roar.py --config-name spectral_ig dataset=oxfordpet
+
+# Measure computation time
+python scripts/measure_computation_time.py --config-name ig dataset=imagenet
+```
+
+## Citation
+
+Research project for ICML 2026 submission on manifold-aware gradient integration methods for explainable AI.