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Found 4 papers, 3 papers with code
Manifold Topology Divergence: a Framework for Comparing Data Manifolds
We develop a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models.
Manifold Topology Divergence: a Framework for Comparing Data Manifolds.
We propose a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models.
Aligning Latent and Image Spaces to Connect the Unconnectable
In this work, we develop a method to generate infinite high-resolution images with diverse and complex content.
Ranked #1 on
Infinite Image Generation
on LHQ
Topological obstructions in neural networks learning
We define the neural network Topological Obstructions score, "TO-score", with the help of robust topological invariants, barcodes of the loss function, that quantify the "badness" of local minima for gradient-based optimization.
