PURGE: Partition-Aware Unlearning for Removing Spurious-Correlation Generated Errors PURGE is a partitioning strategy applied to existing public datasets (MSCOCO 2017) that separates object-relevant evidence from spurious background cues in LVLMs. This repository hosts the resulting preprocessed retain/forget partitions for direct reuse. NeurIPS 2026 Evaluations & Datasets Track, Submission #2701. Hosted under an anonymous account for double-blind review; will be transferred… See the full description on the dataset page: https://huggingface.co/datasets/anonnnnnsub/neuripsED_2701.
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Pull with QuantumShield
quantumshield pull anonnnnnsub/neuripsED_2701 Verify integrity
quantumshield verify anonnnnnsub/neuripsED_2701 pip install
pip install quantumshield && quantumshield pull anonnnnnsub/neuripsED_2701 Unverified Model
This model has not been PQC-verified. File integrity cannot be guaranteed against quantum threats.
README.md
neuripsED_2701
PURGE: Partition-Aware Unlearning for Removing Spurious-Correlation Generated Errors PURGE is a partitioning strategy applied to existing public datasets (MSCOCO 2017) that separates object-relevant evidence from spurious background cues in LVLMs. This repository hosts the resulting preprocessed retain/forget partitions for direct reuse. NeurIPS 2026 Evaluations & Datasets Track, Submission #2701. Hosted under an anonymous account for double-blind review; will be transferred… See the full description on the dataset page: https://huggingface.co/datasets/anonnnnnsub/neuripsED_2701.
Intended Uses
This model is registered on the QuantaMrkt quantum-safe registry. This model has not yet been PQC-verified.
Quick Start
# Install the CLI pip install quantumshield # Pull the model quantumshield pull anonnnnnsub/neuripsED_2701 # Verify file integrity quantumshield verify anonnnnnsub/neuripsED_2701
About
PURGE: Partition-Aware Unlearning for Removing Spurious-Correlation Generated Errors PURGE is a partitioning strategy applied to existing public datasets (MSCOCO 2017) that separates object-relevant evidence from spurious background cues in LVLMs. This repository hosts the resulting preprocessed retain/forget partitions for direct reuse. NeurIPS 2026 Evaluations & Datasets Track, Submission #2701. Hosted under an anonymous account for double-blind review; will be transferred… See the full description on the dataset page: https://huggingface.co/datasets/anonnnnnsub/neuripsED_2701.
Get this model
Pull with QuantumShield
quantumshield pull anonnnnnsub/neuripsED_2701 Verify signatures
quantumshield verify anonnnnnsub/neuripsED_2701