Summary of Unsupervised Representation Learning From Sparse Transformation Analysis, by Yue Song et al.
Unsupervised Representation Learning from Sparse Transformation Analysis
by Yue Song, Thomas Anderson Keller, Yisong Yue, Pietro Perona, Max Welling
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces a novel approach to representation learning based on sequence data, which leverages the principles of coding efficiency, statistical independence, causality, controllability, or symmetry. The authors suggest factorizing the transformations of latent variables into sparse components, allowing for the encoding of input data as distributions of latent activations and subsequent transformation using a probability flow model. This flow model is decomposed into rotational (divergence-free) vector fields and potential flow (curl-free) fields, with a sparsity prior encouraging only a small number of active fields at any instant. The model is trained unsupervised using a variational objective, resulting in disentangled representations that combine independent factors and transformation primitives. This approach is interpreted as learning approximately equivariant representations when viewing the transformations as symmetries. The authors demonstrate state-of-the-art performance on sequence transformation datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to learn representation from sequence data by breaking down the transformations of latent variables into simple parts. It uses a special kind of model called probability flow, which is made up of different vector fields that don’t change size and curl-free fields that don’t change direction. The goal is to get representations that are independent and can be transformed in a meaningful way. This approach leads to state-of-the-art results on datasets with sequence transformations. |
Keywords
» Artificial intelligence » Probability » Representation learning » Unsupervised
