Parameter-tuning-free data entry error unlearning with adaptive
  selective synaptic dampening
Stefan Schoepf Jack Foster Alexandra Brintrup
Data entry constitutes a fundamental component of the machine learning
pipeline, yet it frequently results in the introduction of labelling errors.
When a model has been trained on a dataset containing such errors its
performance is reduced. This leads to the challenge of efficiently unlearning
the influence of the erroneous data to improve the model performance without
needing to completely retrain the model. While model editing methods exist for
cases in which the correct label for a wrong entry is known, we focus on the
case of data entry errors where we do not know the correct labels for the
erroneous data. Our contribution is twofold. First, we introduce an extension
to the selective synaptic dampening unlearning method that removes the need for
parameter tuning, making unlearning accessible to practitioners. We demonstrate
the performance of this extension, adaptive selective synaptic dampening
(ASSD), on various ResNet18 and Vision Transformer unlearning tasks. Second, we
demonstrate the performance of ASSD in a supply chain delay prediction problem
with labelling errors using real-world data where we randomly introduce various
levels of labelling errors. The application of this approach is particularly
compelling in industrial settings, such as supply chain management, where a
significant portion of data entry occurs manually through Excel sheets,
rendering it error-prone. ASSD shows strong performance on general unlearning
benchmarks and on the error correction problem where it outperforms fine-tuning
for error correction.