mlsanityData to 7 Oct 2026

Unsupervised Learning of Structured Representations via Closed-Loop Transcription

arXiv 2210.16782 ↗First posted 30 Oct 2022cs.CVcited by 7

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Abstract (arXiv)

This paper proposes an unsupervised method for learning a unified representation that serves both discriminative and generative purposes. While most existing unsupervised learning approaches focus on a representation for only one of these two goals, we show that a unified representation can enjoy the mutual benefits of having both. Such a representation is attainable by generalizing the recently proposed textitclosed-loop transcription framework, known as CTRL, to the unsupervised setting. This entails solving a constrained maximin game over a rate reduction objective that expands features of all samples while compressing features of augmentations of each sample. Through this process, we see discriminative low-dimensional structures emerge in the resulting representations. Under comparable experimental conditions and network complexities, we demonstrate that these structured representations enable classification performance close to state-of-the-art unsupervised discriminative representations, and conditionally generated image quality significantly higher than that of state-of-the-art unsupervised generative models. Source code can be found at https://github.com/Delay-Xili/uCTRL.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 34 of its 48 references that are in the map. First posted on arXiv 30 Oct 2022.

Builds on

34 of 48 references.

References · 34

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22 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 0; 2025: 1; 2026: 1.

7 papers.

  1. 2023Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Modelscited by 22
  2. 2025Simplifying DINO via Coding Rate Regularizationcited by 20
  3. 2023Top-Down Visual Attention from Analysis by Synthesiscited by 19
  4. 2023EMP-SSL: Towards Self-Supervised Learning in One Training Epochcited by 18
  5. 2023Closed-Loop Transcription via Convolutional Sparse Codingcited by 4
  6. 2023Unifying Self-Supervised Clustering and Energy-Based Modelscited by 2
  7. 2026On the Limits of Maximal Coding Rate Reduction for Out-of-Distribution Generalisationcited by 0