mlsanityData to 7 Oct 2026

Rotation and Translation Invariant Representation Learning with Implicit Neural Representations

arXiv 2304.13995 ↗First posted 27 Apr 2023cs.CV cs.AIcited by 4

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Abstract (arXiv)

In many computer vision applications, images are acquired with arbitrary or random rotations and translations, and in such setups, it is desirable to obtain semantic representations disentangled from the image orientation. Examples of such applications include semiconductor wafer defect inspection, plankton microscope images, and inference on single-particle cryo-electron microscopy (cryo-EM) micro-graphs. In this work, we propose Invariant Representation Learning with Implicit Neural Representation (IRL-INR), which uses an implicit neural representation (INR) with a hypernetwork to obtain semantic representations disentangled from the orientation of the image. We show that IRL-INR can effectively learn disentangled semantic representations on more complex images compared to those considered in prior works and show that these semantic representations synergize well with SCAN to produce state-of-the-art unsupervised clustering results.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 39 of its 68 references that are in the map. First posted on arXiv 27 Apr 2023.

Builds on

39 of 68 references.

References · 39

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27 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2023Image Clustering Conditioned on Text Criteriacited by 26
  2. 2024SOE: SO(3)-Equivariant 3D MRI Encodingcited by 1
  3. 2024Level-Set Parameters: Novel Representation for 3D Shape Analysiscited by 0
  4. 2025Joint Denoising of Cryo-EM Projection Images using Polar Transformerscited by 0