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Found 5 papers, 3 papers with code
Delaunay Component Analysis for Evaluation of Data Representations
Advanced representation learning techniques require reliable and general evaluation methods.
GraphDCA -- a Framework for Node Distribution Comparison in Real and Synthetic Graphs
We argue that when comparing two graphs, the distribution of node structural features is more informative than global graph statistics which are often used in practice, especially to evaluate graph generative models.
Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning
Most methods learn state representations by utilizing losses based on the reconstruction of the raw observations from a lower-dimensional latent space.
GeomCA: Geometric Evaluation of Data Representations
Evaluating the quality of learned representations without relying on a downstream task remains one of the challenges in representation learning.
Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap
We present a framework for visual action planning of complex manipulation tasks with high-dimensional state spaces, focusing on manipulation of deformable objects.
