mlsanityData to 7 Oct 2026

Progressive Stage-wise Learning for Unsupervised Feature Representation Enhancement

arXiv 2106.05554 ↗First posted 10 Jun 2021cs.CVcited by 3

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Abstract (arXiv)

Unsupervised learning methods have recently shown their competitiveness against supervised training. Typically, these methods use a single objective to train the entire network. But one distinct advantage of unsupervised over supervised learning is that the former possesses more variety and freedom in designing the objective. In this work, we explore new dimensions of unsupervised learning by proposing the Progressive Stage-wise Learning (PSL) framework. For a given unsupervised task, we design multilevel tasks and define different learning stages for the deep network. Early learning stages are forced to focus on lowlevel tasks while late stages are guided to extract deeper information through harder tasks. We discover that by progressive stage-wise learning, unsupervised feature representation can be effectively enhanced. Our extensive experiments show that PSL consistently improves results for the leading unsupervised learning methods.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 37 of its 52 references that are in the map. First posted on arXiv 10 Jun 2021.

Builds on

37 of 52 references.

References · 37

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25 more
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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1.

3 papers.

  1. 2022Beyond Instance Discrimination: Relation-aware Contrastive Self-supervised Learningcited by 4
  2. 2024Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Trainingcited by 3
  3. 2021Self-Supervised Modality-Aware Multiple Granularity Pre-Training for RGB-Infrared Person Re-Identificationcited by 0