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Found 8 papers, 3 papers with code
MMP++: Motion Manifold Primitives with Parametric Curve Models
Motion Manifold Primitives (MMP), a manifold-based approach for encoding basic motion skills, can produce diverse trajectories, enabling the system to adapt to unseen constraints.
On Explicit Curvature Regularization in Deep Generative Models
We propose a family of curvature-based regularization terms for deep generative model learning.
A Geometric Perspective on Autoencoders
Given a set of high-dimensional data points that approximately lie on some lower-dimensional manifold, an autoencoder learns the \textit{manifold} and its \textit{coordinate chart}, simultaneously.
Evaluating Out-of-Distribution Detectors Through Adversarial Generation of Outliers
A reliable evaluation method is essential for building a robust out-of-distribution (OOD) detector.
Neighborhood Reconstructing Autoencoders
Unlike existing graph-based methods that attempt to encode the training data to some prescribed latent space distribution -- one consequence being that only the encoder is the object of the regularization -- NRAE merges local connectivity information contained in the neighborhood graphs with local quadratic approximations of the decoder function to formulate a new neighborhood reconstruction loss.
Regularized Autoencoders for Isometric Representation Learning
The recent success of autoencoders for representation learning can be traced in large part to the addition of a regularization term.
Adversarial Distributions Against Out-of-Distribution Detectors
As an outlier may deviate from the training distribution in unexpected ways, an ideal OOD detector should be able to detect all types of outliers.
A Statistical Manifold Framework for Point Cloud Data
The only requirement on the part of the user is the choice of a meaningful underlying probability distribution, which is more intuitive and natural to make than what is required in existing ad hoc formulations.
