Upload README.md with huggingface_hub

README.md

@@ -6,7 +6,7 @@ Research project backup for Latent Gradient Integrated Gradients (Latent GIG) -
 
 ```bash
 # huggingface_hub 설치 (필요시)
-pip install huggingface_hub
+pip install huggingface-hub==0.20.2
 
 # 다운로드
 huggingface-cli download leekwoon/latent_gig_backup --repo-type dataset --local-dir ./latent_gig_data
@@ -24,10 +24,20 @@ cat data.tar.gz.part_* | tar -xzvf -
 ```
 latent_gig/
 ├── cleanig/              # Core implementation modules
-│   ├── explainer/        # Explainability algorithms (IG, GIG, AGI, Latent GIG)
+│   ├── explainer/        # Explainability algorithms
+│   │   ├── ig.py         # Integrated Gradients
+│   │   ├── gig.py        # Guided IG
+│   │   ├── agi.py        # Adversarial GI
+│   │   ├── eig.py        # Expected IG
+│   │   ├── mig.py        # Manifold IG
+│   │   ├── saliency.py   # Saliency maps
+│   │   ├── latent_gig.py # Latent GIG (main method)
+│   │   ├── soft_latent_gig.py
+│   │   ├── superpixel_gig.py
+│   │   └── smooth_wrapper.py
 │   ├── dataset/          # Dataset utilities (ImageNet, OxfordPet, OxfordFlower)
 │   ├── classifier/       # Classifier utilities
-│   ├── metric/           # Evaluation metrics (DiffID)
+│   ├── metric/           # Evaluation metrics (DiffID, DiffROAR)
 │   ├── mar_vae/          # MAR-VAE implementation
 │   └── vae_wrapper.py    # VAE wrapper for different models
 ├── configs/              # Configuration files
@@ -35,21 +45,32 @@ latent_gig/
 │   ├── ig.yaml           # Integrated Gradients config
 │   ├── gig.yaml          # Guided IG config
 │   ├── agi.yaml          # Adversarial GI config
+│   ├── eig.yaml          # Expected IG config
+│   ├── mig.yaml          # Manifold IG config
 │   ├── latent_gig.yaml   # Latent GIG config
+│   ├── smooth_*.yaml     # Smooth variants (grad, ig, gig, latent_gig)
+│   ├── saliency.yaml     # Saliency config
 │   └── superpixel_gig.yaml
-├── notebooks/            # Jupyter notebooks
-│   └── analysis/         # Result analysis notebooks
+├── data/                 # Dataset files
+│   ├── cifar-10-*/       # CIFAR-10 dataset
+│   └── cifar-100-*/      # CIFAR-100 dataset
 ├── results/              # Experiment results
 │   ├── benchmark_diffid/ # DiffID benchmark results
-│   └── classifier_*/     # Trained classifiers
+│   ├── benchmark_diffroar/ # DiffROAR benchmark results
+│   ├── classifier_oxfordpet/   # Trained OxfordPet classifiers
+│   ├── classifier_oxfordflower/ # Trained OxfordFlower classifiers
+│   ├── tests/            # Test results with path visualizations
+│   └── icml26_*/         # ICML26 experiment results
 ├── scripts/              # Execution scripts
-│   ├── baselines/        # Baseline method scripts
-│   ├── run_baselines.sh  # Run all baselines
-│   ├── run_latent_gig.sh # Run Latent GIG experiments
+│   ├── benchmark_diffid/ # DiffID benchmark scripts
+│   ├── benchmark_diffroar/ # DiffROAR benchmark scripts
+│   ├── tests/            # Test scripts
+│   ├── diffid.py         # DiffID evaluation
+│   ├── diff_roar.py      # DiffROAR evaluation
 │   ├── analyze_results.py # Result analysis tool
-│   └── diffid.py         # DiffID evaluation
-├── papers/               # Related papers
-└── requirements.txt      # Python dependencies
+│   └── upload_to_hf.sh   # HuggingFace upload script
+├── requirements.txt      # Python dependencies
+└── setup.py              # Package setup
 ```
 
 ## Key Components
@@ -58,7 +79,11 @@ latent_gig/
 - **IG (Integrated Gradients)**: Standard integrated gradients
 - **GIG (Guided IG)**: Guided version with adversarial paths
 - **AGI (Adversarial GI)**: Adversarial gradient integration
+- **EIG (Expected IG)**: Expected integrated gradients
+- **MIG (Manifold IG)**: Manifold-based integrated gradients
+- **Saliency**: Basic saliency maps
 - **Latent GIG**: Our method using latent space paths with VAE models
+- **Smooth variants**: SmoothGrad, SmoothIG, SmoothGIG, SmoothLatentGIG
 
 ### VAE Models Supported
 - **SD1**: Stable Diffusion v1 VAE
@@ -70,9 +95,11 @@ latent_gig/
 - ImageNet (subset)
 - Oxford-IIIT Pet Dataset
 - Oxford Flower Dataset
+- CIFAR-10, CIFAR-100
 
 ### Evaluation Metrics
-- DiffID (Diffusion-generated Image Detection)
+- **DiffID**: Diffusion-based deletion metric
+- **DiffROAR**: Diffusion-based ROAR metric
 - Insertion/Deletion curves
 - Attribution quality metrics