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README.md
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# DiffIG GIG Backup
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Research project backup for DiffIG (Diffusion-based Integrated Gradients) and GIG (Guided Integrated Gradients) - advanced explainability methods for image classifiers.
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## Download & Extract
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```bash
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# huggingface_hub 설치 (필요시)
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pip install huggingface_hub
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# 다운로드
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huggingface-cli download leekwoon/diffig_gig_backup --repo-type dataset --local-dir ./diffig_data
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# 무결성 확인 (선택사항)
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cd diffig_data
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md5sum -c checksums.md5
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# 파일 합치기 및 압축 해제
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cat data.tar.gz.part_* | tar -xzvf -
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```
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## Directory Structure
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```
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diffig/
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├── diffig/ # Core implementation modules
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│ ├── explainer/ # Explainability algorithms
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│ │ ├── ig.py # Integrated Gradients
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│ │ ├── ig2.py # Improved IG
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│ │ ├── gig.py # Guided IG
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│ │ ├── agi.py # Adversarial GI
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│ │ ├── diffig.py # DiffIG (our method)
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│ │ ├── mig.py # Manifold IG
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│ │ ├── big.py # Boundary IG
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│ │ ├── eig.py # Enhanced IG
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│ │ ├── sg.py # Smooth Gradients
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│ │ ├── spi.py # SmoothGrad Path Integration
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│ │ ├── fullgrad.py # FullGrad
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│ │ └── gradcam.py # GradCAM
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│ ├── dataset/ # Dataset utilities
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│ │ ├── oxfordpet_dataset.py
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│ │ ├── oxfordflower_dataset.py
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│ │ └── miniimagenet_dataset.py
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│ ├── classifier/ # Classifier utilities
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│ ├── diffusion/ # Diffusion model components
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│ ├── metric/ # Evaluation metrics
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│ │ ├── diffid.py # DiffID metric
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│ │ ├── complexity.py # Complexity metrics
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│ │ └── path_stability.py
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│ ├── mar_vae/ # MAR-VAE implementation
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│ └── mig_vae/ # MIG-VAE implementation
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├── configs/ # Configuration files
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│ ├── benchmark/ # Benchmark configs for each method
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│ │ ├── ig.yaml
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│ │ ├── gig.yaml
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│ │ ├── agi.yaml
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│ │ ├── diffig.yaml
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│ │ ├── mig.yaml
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│ │ └── ...
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│ ├── classifier/ # Classifier training configs
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│ └── diffig/ # DiffIG specific configs
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├── pipelines/ # Execution pipelines
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│ ├── benchmark/ # Benchmark evaluation scripts
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│ │ ├── diffid.py
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│ │ ├── save_attributions.py
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│ │ └── eval_attributions.py
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│ ├── classifier/ # Classifier training pipelines
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│ └── diffig/ # DiffIG training pipelines
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├── notebooks/ # Jupyter notebooks
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│ ├── cvpr26/ # CVPR 2026 figures
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│ └── analysis notebooks
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├── results/ # Experiment results
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│ ├── benchmark/ # Benchmark results
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│ ├── attributions/ # Saved attributions
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│ ├── classifier_*/ # Trained classifiers
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│ └── vae_*/ # Trained VAE models
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├── scripts/ # Shell scripts
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│ ├── benchmark_cvpr26.sh
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│ ├── analyze_baseline_results.py
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│ └── analyze_diffig_results.py
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├── papers/ # Related papers
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└── requirements.txt # Python dependencies
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```
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## Key Components
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### Explainer Methods
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- **IG**: Integrated Gradients
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- **GIG**: Guided Integrated Gradients
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- **AGI**: Adversarial Gradient Integration
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- **DiffIG**: Diffusion-based IG (our method)
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- **MIG**: Manifold Integrated Gradients
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- **BIG**: Boundary Integrated Gradients
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- **EIG**: Enhanced Integrated Gradients
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- **SG**: SmoothGrad
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- **SPI**: SmoothGrad Path Integration
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- **FullGrad**: Full-Gradient Saliency
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- **GradCAM**: Gradient-weighted Class Activation Mapping
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### Datasets Supported
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- Oxford-IIIT Pet Dataset (37 categories)
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- Oxford Flower Dataset (102 categories)
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- Mini-ImageNet (100 classes)
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### Models Supported
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- ResNet18
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- ResNet34
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- VGG16
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- Inception
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### Evaluation Metrics
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- **DiffID**: Diffusion-based attribution quality metric
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- **Insertion/Deletion**: Standard faithfulness metrics
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- **Complexity**: Attribution complexity measures
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- **Path Stability**: Path consistency metrics
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## Key Features
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- Comprehensive benchmark of attribution methods
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- Novel DiffIG method using diffusion models for improved paths
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- Support for multiple datasets and architectures
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- Extensive evaluation metrics including novel DiffID metric
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- Reproducible experiments with configuration files
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## Citation
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This is a research project for developing and benchmarking improved explainability methods for image classifiers, with focus on path-based attribution methods and diffusion models.
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