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Found 3 papers, 3 papers with code
Masked Discriminators for Content-Consistent Unpaired Image-to-Image Translation
In this work, we show that masking the inputs of a global discriminator for both domains with a content-based mask is sufficient to reduce content inconsistencies significantly.
Don't miss the Mismatch: Investigating the Objective Function Mismatch for Unsupervised Representation Learning
Thereby we disclose dependencies of the objective function mismatch across several pretext and target tasks with respect to the pretext model's representation size, target model complexity, pretext and target augmentations as well as pretext and target task types.
CSNNs: Unsupervised, Backpropagation-free Convolutional Neural Networks for Representation Learning
This work combines Convolutional Neural Networks (CNNs), clustering via Self-Organizing Maps (SOMs) and Hebbian Learning to propose the building blocks of Convolutional Self-Organizing Neural Networks (CSNNs), which learn representations in an unsupervised and Backpropagation-free manner.
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