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The dataset viewer is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
pdf: binary
__key__: string
__url__: string
py: null
to
{'py': Value('binary'), '__key__': Value('string'), '__url__': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2092, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2192, in cast_table_to_features
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              pdf: binary
              __key__: string
              __url__: string
              py: null
              to
              {'py': Value('binary'), '__key__': Value('string'), '__url__': Value('string')}
              because column names don't match

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Spectral IG Backup

Research project backup for Spectral Integrated Gradients (Spectral IG / SIG) - an explainability method that uses SVD-based coarse-to-fine path construction for image classifiers.

Download & Extract

# huggingface_hub 설치 (필요시)
pip install huggingface-hub==0.20.2

# 다운로드
huggingface-cli download leekwoon/260204_sig_backup --repo-type dataset --local-dir ./spectral_ig_data

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

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

Directory Structure

spectral_ig/
├── cleanig/              # Core implementation modules
│   ├── explainer/        # Explainability algorithms
│   │   ├── ig.py         # Integrated Gradients
│   │   ├── spectral_ig.py # Spectral IG (main contribution)
│   │   ├── gig.py        # Guided IG
│   │   ├── big.py        # Blur IG
│   │   ├── agi.py        # Adversarial GI
│   │   ├── eig.py        # Enhanced IG
│   │   ├── mig.py        # Manifold IG
│   │   ├── samp.py       # SAMP
│   │   └── path_utils.py # Path generators (Linear, Spectral, Blur, Geodesic)
│   ├── dataset/          # Dataset utilities (ImageNet, OxfordPet, CIFAR10)
│   ├── classifier/       # Classifier utilities
│   └── metric/           # Evaluation metrics
├── configs/              # Hydra configuration files
│   ├── dataset/          # Dataset configs
│   ├── ig.yaml           # IG config
│   ├── spectral_ig.yaml  # Spectral IG config
│   ├── gig.yaml          # Guided IG config
│   └── ...
├── results/              # Experiment results
│   ├── icml26_figures/   # Paper figures
│   ├── roar/             # ROAR evaluation results
│   ├── diffid/           # DiffID evaluation results
│   ├── saliency_maps/    # Generated saliency maps
│   └── classifier_*/     # Trained classifiers
├── scripts/              # Execution scripts
│   ├── icml26_figures/   # Figure generation scripts
│   ├── roar.py           # ROAR evaluation
│   ├── diffid.py         # DiffID evaluation
│   └── measure_computation_time.py
├── saliency/             # Pre-computed saliency maps
└── requirements.txt      # Python dependencies

Key Components

Explainers

IG (Integrated Gradients): Standard linear path integration
Spectral IG: SVD-based coarse-to-fine path construction (main contribution)
GIG (Guided IG): Gradient-guided adaptive paths
BIG (Blur IG): Gaussian blur-based paths
AGI (Adversarial GI): Adversarial gradient integration
EIG (Enhanced IG): Latent space paths
MIG (Manifold IG): Geodesic paths on VAE manifold
SAMP: Adaptive sampling method

Spectral IG Key Parameters

num_steps: Number of integration steps (default: 200)
overlap: Overlap parameter for SVD component activation (default: 0.4)
- overlap=1.0: All SVs scale together (like linear IG)
- overlap=0.5: Partial overlap, skeleton first
- overlap=0.0: Sequential, hard thresholding

Datasets

ImageNet (ILSVRC2012)
Oxford-IIIT Pet Dataset
CIFAR-10

Evaluation Metrics

ROAR (RemOve And Retrain): Measures attribution faithfulness
DiffID (Insertion/Deletion): Measures attribution quality
Computation time benchmarks

Usage

# Install
pip install -e .

# Run Spectral IG
python scripts/diffid.py --config-name spectral_ig dataset=imagenet

# Run ROAR evaluation
python scripts/roar.py --config-name spectral_ig dataset=oxfordpet

# Generate paper figures
python scripts/icml26_figures/compare_ig_sig_frequency.py --config-name ig dataset=oxfordpet

Citation

Spectral Integrated Gradients: SVD-based Coarse-to-Fine Path Construction for Explainable AI (ICML 2026 submission)

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