mlsanityData to 7 Oct 2026

Domain-Generalizable Multiple-Domain Clustering

arXiv 2301.13530 ↗First posted 31 Jan 2023cs.LG cs.CVcited by 7

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Abstract (arXiv)

This work generalizes the problem of unsupervised domain generalization to the case in which no labeled samples are available (completely unsupervised). We are given unlabeled samples from multiple source domains, and we aim to learn a shared predictor that assigns examples to semantically related clusters. Evaluation is done by predicting cluster assignments in previously unseen domains. Towards this goal, we propose a two-stage training framework: (1) self-supervised pre-training for extracting domain invariant semantic features. (2) multi-head cluster prediction with pseudo labels, which rely on both the feature space and cluster head prediction, further leveraging a novel prediction-based label smoothing scheme. We demonstrate empirically that our model is more accurate than baselines that require fine-tuning using samples from the target domain or some level of supervision. Our code is available at https://github.com/AmitRozner/domain-generalizable-multiple-domain-clustering.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 36 of its 58 references that are in the map. First posted on arXiv 31 Jan 2023.

Builds on

36 of 58 references.

References · 36

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24 more
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Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 3.

7 papers.

  1. 2025SUMO: Subspace-Aware Moment-Orthogonalization for Accelerating Memory-Efficient LLM Trainingcited by 16
  2. 2024Unveiling Multiple Descents in Unsupervised Autoencoderscited by 3
  3. 2025LORENZA: Enhancing Generalization in Low-Rank Gradient LLM Training via Efficient Zeroth-Order Adaptive SAMcited by 3
  4. 2024Self Supervised Correlation-based Permutations for Multi-View Clusteringcited by 1
  5. 2023Neuronal Cell Type Classification using Deep Learningcited by 0
  6. 2024SEL-CIE: Knowledge-Guided Self-Supervised Learning Framework for CIE-XYZ Reconstruction from Non-Linear sRGB Imagescited by 0
  7. 2025Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labelscited by 0