Summary of Unsupervised Disentanglement Of Content and Style Via Variance-invariance Constraints, by Yuxuan Wu et al.
Unsupervised Disentanglement of Content and Style via Variance-Invariance Constraints
by Yuxuan Wu, Ziyu Wang, Bhiksha Raj, Gus Xia
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 We present an unsupervised method called V3 that learns disentangled content and style representations from sequences of observations. Unlike most disentanglement algorithms, our approach relies on domain-general statistical differences between content and style. Specifically, we observe that content varies more within a sample but maintains an invariant vocabulary across samples, whereas style remains relatively invariant within a sample but exhibits more significant variation across different samples. Our V3 method integrates this inductive bias into an encoder-decoder architecture, demonstrating strong disentanglement performance compared to existing unsupervised methods. Experimental results show that V3 generalizes across multiple domains and modalities, successfully learning disentangled content and style representations from music audio, images of hand-written digits, and simple animations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We developed a new way to understand complex data without needing labels or special knowledge. Our method, called V3, looks for patterns in the data that tell it what’s important (content) versus what’s just different (style). We tested V3 on music, handwritten digits, and animations, and it worked well across all these domains. This means our method can learn to recognize things like pitch and timbre in music, or digit and color in written numbers, without needing special training data. Our results show that V3 is better at understanding new, unseen data than other methods that need labels. |
Keywords
* Artificial intelligence * Encoder decoder * Unsupervised
