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Found 8 papers, 4 papers with code
Latent Space Translation via Semantic Alignment
While different neural models often exhibit latent spaces that are alike when exposed to semantically related data, this intrinsic similarity is not always immediately discernible.
From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication
It has been observed that representations learned by distinct neural networks conceal structural similarities when the models are trained under similar inductive biases.
Leveraging sparse and shared feature activations for disentangled representation learning
Recovering the latent factors of variation of high dimensional data has so far focused on simple synthetic settings.
Bootstrapping Parallel Anchors for Relative Representations
The use of relative representations for latent embeddings has shown potential in enabling latent space communication and zero-shot model stitching across a wide range of applications.
Relative representations enable zero-shot latent space communication
Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations.
Unsupervised Source Separation via Bayesian Inference in the Latent Domain
State of the art audio source separation models rely on supervised data-driven approaches, which can be expensive in terms of labeling resources.
Ranked #1 on
Music Source Separation
on Slakh2100
CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation
Generating shapes using natural language can enable new ways of imagining and creating the things around us.
Learning disentangled representations via product manifold projection
We propose a novel approach to disentangle the generative factors of variation underlying a given set of observations.
