mlsanityData to 7 Oct 2026

A survey of manifold learning and its applications for multimedia

arXiv 2310.12986 ↗First posted 8 Sep 2023cs.MM cs.AIcited by 1

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Abstract (arXiv)

Manifold learning is an emerging research domain of machine learning. In this work, we give an introduction into manifold learning and how it is employed for important application fields in multimedia.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 35 of its 61 references that are in the map. First posted on arXiv 8 Sep 2023.

Builds on

35 of 61 references.

References · 35

  1. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  2. 2022Diffusion Models in Vision: A Surveycited by 1,008
  3. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  4. 2018Riemannian Adaptive Optimization Methodscited by 187
  5. 2020Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualizationcited by 170
  6. 2016Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiationcited by 93
  7. 2020Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transformcited by 79
  8. 2020Embedding Propagation: Smoother Manifold for Few-Shot Classificationcited by 79
  9. 2019Trivializations for Gradient-Based Optimization on Manifoldscited by 78
  10. 2020A Decade Survey of Content Based Image Retrieval using Deep Learningcited by 72
  11. 2018geomstats: a Python Package for Riemannian Geometry in Machine Learningcited by 71
  12. 2021Deep Learning for Instance Retrieval: A Surveycited by 70
23 more
  1. 2022Theseus: A Library for Differentiable Nonlinear Optimizationcited by 64
  2. 2022Matching Normalizing Flows and Probability Paths on Manifoldscited by 57
  3. 2020Differentiating through the Fr'echet Meancited by 52
  4. 2021Hyperbolic Busemann Learning with Ideal Prototypescited by 40
  5. 2018Mining on Manifolds: Metric Learning without Labelscited by 36
  6. 2017Riemannian approach to batch normalizationcited by 35
  7. 2021Manifold Topology Divergence: a Framework for Comparing Data Manifoldscited by 31
  8. 2021Projective Manifold Gradient Layer for Deep Rotation Regressioncited by 31
  9. 2022LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihoodcited by 30
  10. 2022Everything is There in Latent Space: Attribute Editing and Attribute Style Manipulation by StyleGAN Latent Space Explorationcited by 21
  11. 2022Intrinsic Neural Fields: Learning Functions on Manifoldscited by 20
  12. 2019Gaussians on Riemannian Manifolds: Applications for Robot Learning and Adaptive Controlcited by 17
  13. 2022Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reductioncited by 14
  14. 2018Manifold Optimization Over the Set of Doubly Stochastic Matrices: A Second-Order Geometrycited by 12
  15. 2019Dilated Convolutional Neural Networks for Sequential Manifold-valued Datacited by 11
  16. 2021Improving Metric Dimensionality Reduction with Distributed Topologycited by 10
  17. 2021Directed Graph Embeddings in Pseudo-Riemannian Manifoldscited by 9
  18. 2020ManifoldNorm: Extending normalizations on Riemannian Manifoldscited by 8
  19. 2021Adaptive Feature Interpolation for Low-Shot Image Generationcited by 6
  20. 2020Rethinking conditional GAN training: An approach using geometrically structured latent manifoldscited by 5
  21. 2020Unsupervised Geometric Disentanglement for Surfaces via CFAN-VAEcited by 3
  22. 2020MVC-Net: A Convolutional Neural Network Architecture for Manifold-Valued Images With Applicationscited by 2
  23. 2020KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognitioncited by 2

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  1. 2024Noisy Data Visualization using Functional Data Analysiscited by 0