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Found 4 papers, 1 papers with code
Machine Unlearning via Null Space Calibration
Current research centres on efficient unlearning to erase the influence of data from the model and neglects the subsequent impacts on the remaining data.
Divide and Ensemble: Progressively Learning for the Unknown
In the wheat nutrient deficiencies classification challenge, we present the DividE and EnseMble (DEEM) method for progressive test data predictions.
Privacy and Fairness in Federated Learning: on the Perspective of Trade-off
Federated learning (FL) has been a hot topic in recent years.
Model Inversion Attack against Transfer Learning: Inverting a Model without Accessing It
However, they may not mean that transfer learning models are impervious to model inversion attacks.
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