Original article: Selective Amnesia using Contrastive Subnet Erasure for Class Level Unlearning in Vision Models (CVPR 2026)
PDF URL: https://openaccess.thecvf.com/content/CVPR2026/papers/Pramanik_Selective_Amnesia_using_Contrastive_Subnet_Erasure_for_Class_Level_Unlearning_CVPR_2026_paper.pdf
Metadata URL: https://github.com/Mokshithdv25/cse-calibrated-reproduction-study/blob/main/README.md
Code URL: https://github.com/Mokshithdv25/cse-calibrated-reproduction-study
Scientific domain: Machine learning, computer vision, and computational reproducibility
Programming language: Python
Suggested editor:
This submission reports an independent, fail-closed replication and diagnostic study of CSE. It separates targeted channel attenuation from retain-only classifier-head recalibration using factorial controls, feature probes, and cross-dataset validation. The study includes validated positive and negative replication evidence and documents the limits of the original claim.
Original article: Selective Amnesia using Contrastive Subnet Erasure for Class Level Unlearning in Vision Models (CVPR 2026)
PDF URL: https://openaccess.thecvf.com/content/CVPR2026/papers/Pramanik_Selective_Amnesia_using_Contrastive_Subnet_Erasure_for_Class_Level_Unlearning_CVPR_2026_paper.pdf
Metadata URL: https://github.com/Mokshithdv25/cse-calibrated-reproduction-study/blob/main/README.md
Code URL: https://github.com/Mokshithdv25/cse-calibrated-reproduction-study
Scientific domain: Machine learning, computer vision, and computational reproducibility
Programming language: Python
Suggested editor:
This submission reports an independent, fail-closed replication and diagnostic study of CSE. It separates targeted channel attenuation from retain-only classifier-head recalibration using factorial controls, feature probes, and cross-dataset validation. The study includes validated positive and negative replication evidence and documents the limits of the original claim.