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Found 6 papers, 5 papers with code
Robust Autoencoders for Collective Corruption Removal
Robust PCA is a standard tool for learning a linear subspace in the presence of sparse corruption or rare outliers.
Interpretable Deep Learning Methods for Multiview Learning
We propose iDeepViewLearn (Interpretable Deep Learning Method for Multiview Learning) for learning nonlinear relationships in data from multiple views while achieving feature selection.
Deep Random Projector: Accelerated Deep Image Prior
Deep image prior (DIP) has shown great promise in tackling a variety of image restoration (IR) and general visual inverse problems, needing no training data.
Blind Image Deblurring with Unknown Kernel Size and Substantial Noise
Blind image deblurring (BID) has been extensively studied in computer vision and adjacent fields.
Early Stopping for Deep Image Prior
In this regard, the majority of DIP works for vision tasks only demonstrates the potential of the models -- reporting the peak performance against the ground truth, but provides no clue about how to operationally obtain near-peak performance without access to the groundtruth.
Self-Validation: Early Stopping for Single-Instance Deep Generative Priors
Recent works have shown the surprising effectiveness of deep generative models in solving numerous image reconstruction (IR) tasks, even without training data.
