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Found 6 papers, 6 papers with code
Situation Awareness for Driver-Centric Driving Style Adaptation
Furthermore, we found that feature encoders pretrained on our dataset lead to more precise driving behavior modeling.
Towards Unsupervised Representation Learning: Learning, Evaluating and Transferring Visual Representations
Unsupervised representation learning aims at finding methods that learn representations from data without annotation-based signals.
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.
CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains
Unsupervised Domain Adaptation demonstrates great potential to mitigate domain shifts by transferring models from labeled source domains to unlabeled target domains.
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on MuLane
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.
