# Machine Unlearning on ImageNet 1k retain forget (val test) Source: [Machine Unlearning on ImageNet 1k retain forget (val test)]() Collection index: [llms.txt]() Data updated: 2026-02-26T05:20:53.951Z Evaluation of Machine Unlearning effectiveness on the ImageNet 1k dataset, specifically using the 'retain forget' subset for validation/testing, reporting metrics related to retention accuracy and unlearning success (e.g., RF-JSD, MIA). A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. | Scope | Recorded value | | --- | --- | | Task | Machine Unlearning | | Dataset (including any stated split) | ImageNet 1k retain forget (val test) | | Metric | RF-JSD (x1e4) | | Unit | SCALAR | | Direction | Lower is better | | Results loaded | 0 of 0 | | Coverage | All results currently returned for this benchmark | Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol. ## Metrics - [RF-JSD (x1e4)](): Lower is better; unit: SCALAR. - [Retain Accuracy](): Higher is better; unit: PERCENT. - [MIA Accuracy](): Higher is better; unit: PERCENT. ## Recorded results | Method | RF-JSD (x1e4) | Result date | Report paper | | --- | --- | --- | --- | Page 1 of 1. This is an index page, not the full collection.