DenoGrad refines noisy tabular and time-series data by optimizing inputs via gradients from a fixed model, yielding better downstream predictions on ten real-world datasets while preserving data statistics.
Majumdar, Blind denoising autoencoder, IEEE transactions on neural networks and learning systems 30 (1) (2018) 312–317
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DenoGrad: A Gradient-Based Framework for Data Refinement in Tabular and Time-Series Learning
DenoGrad refines noisy tabular and time-series data by optimizing inputs via gradients from a fixed model, yielding better downstream predictions on ten real-world datasets while preserving data statistics.