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