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VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning Challenges

arXiv 2103.03768 ↗First posted 5 Mar 2021cs.CVcited by 11

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Abstract (arXiv)

We present the first edition of "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" challenges. We offer four data-impaired challenges, where models are trained from scratch, and we reduce the number of training samples to a fraction of the full set. Furthermore, to encourage data efficient solutions, we prohibited the use of pre-trained models and other transfer learning techniques. The majority of top ranking solutions make heavy use of data augmentation, model ensembling, and novel and efficient network architectures to achieve significant performance increases compared to the provided baselines.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 61 of its 75 references that are in the map. First posted on arXiv 5 Mar 2021.

Builds on

61 of 75 references.

References · 61

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38 more
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50 most cited of 61 All 61 (JSON)

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Cited by year

Citing papers per year: 2021: 2; 2022: 5; 2023: 2; 2024: 1; 2025: 1.

11 papers.

  1. 2021Parametric Scattering Networkscited by 15
  2. 2022Equivariance and Invariance Inductive Bias for Learning from Insufficient Datacited by 14
  3. 2022VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challengescited by 8
  4. 2022Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classificationcited by 4
  5. 2022Image Classification with Small Datasets: Overview and Benchmarkcited by 4
  6. 2023VIPriors 3: Visual Inductive Priors for Data-Efficient Deep Learning Challengescited by 4
  7. 2021A Strong Baseline for the VIPriors Data-Efficient Image Classification Challengecited by 2
  8. 2023No Data Augmentation? Alternative Regularizations for Effective Training on Small Datasetscited by 1
  9. 20222nd Place Solution for ICCV 2021 VIPriors Image Classification Challenge: An Attract-and-Repulse Learning Approachcited by 0
  10. 2024VIPriors 4: Visual Inductive Priors for Data-Efficient Deep Learning Challengescited by 0
  11. 2025Data-Efficient Challenges in Visual Inductive Priors: A Retrospectivecited by 0