mlsanityData to 7 Oct 2026

Prototype-based Dataset Comparison

arXiv 2309.02401 ↗First posted 5 Sep 2023cs.CV cs.MMcited by 9

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Abstract (arXiv)

Dataset summarisation is a fruitful approach to dataset inspection. However, when applied to a single dataset the discovery of visual concepts is restricted to those most prominent. We argue that a comparative approach can expand upon this paradigm to enable richer forms of dataset inspection that go beyond the most prominent concepts. To enable dataset comparison we present a module that learns concept-level prototypes across datasets. We leverage self-supervised learning to discover these prototypes without supervision, and we demonstrate the benefits of our approach in two case-studies. Our findings show that dataset comparison extends dataset inspection and we hope to encourage more works in this direction. Code and usage instructions available at https://github.com/Nanne/ProtoSim

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 35 of its 49 references that are in the map. First posted on arXiv 5 Sep 2023.

Builds on

35 of 49 references.

References · 35

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  4. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
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  8. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  9. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  10. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,845
  11. 2021BEiT: BERT Pre-Training of Image Transformerscited by 2,360
  12. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
23 more
  1. 2020Concept Bottleneck Modelscited by 1,022
  2. 2018This Looks Like That: Deep Learning for Interpretable Image Recognitioncited by 731
  3. 2020Prototypical Contrastive Learning of Unsupervised Representationscited by 636
  4. 2022Dataset Distillation by Matching Training Trajectoriescited by 437
  5. 2021Dataset Condensation with Distribution Matchingcited by 362
  6. 2022Post-hoc Concept Bottleneck Modelscited by 289
  7. 2020Data and its (dis)contents: A survey of dataset development and use in machine learning researchcited by 259
  8. 2019On Completeness-aware Concept-Based Explanations in Deep Neural Networkscited by 239
  9. 2016Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clusteringcited by 237
  10. 2020Large image datasets: A pyrrhic win for computer vision?cited by 237
  11. 2017Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictionscited by 234
  12. 2019Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchycited by 156
  13. 2022Rethinking Semantic Segmentation: A Prototype Viewcited by 140
  14. 2019Hyperspherical Prototype Networkscited by 85
  15. 2021A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learningcited by 82
  16. 2018How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrievalcited by 73
  17. 2021PASS: An ImageNet replacement for self-supervised pretraining without humanscited by 51
  18. 2022The Met Dataset: Instance-level Recognition for Artworkscited by 40
  19. 2021Discrete Representations Strengthen Vision Transformer Robustnesscited by 34
  20. 2015Prototypical Priors: From Improving Classification to Zero-Shot Learningcited by 14
  21. 2019Deep clustering with concrete k-meanscited by 11
  22. 2017Hierarchically-Attentive RNN for Album Summarization and Storytellingcited by 10
  23. 2014Mid-level Deep Pattern Miningcited by 3

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 5.

9 papers.

  1. 2023Describing Differences in Image Sets with Natural Languagecited by 51
  2. 2024Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual Knowledgecited by 12
  3. 2025ConceptScope: Characterizing Dataset Bias via Disentangled Visual Conceptscited by 5
  4. 2025Provably Improving Generalization of Few-Shot Models with Synthetic Datacited by 3
  5. 2024Precision at Scale: Domain-Specific Datasets On-Demandcited by 1
  6. 2025Visual Chronicles: Using Multimodal LLMs to Analyze Massive Collections of Imagescited by 1
  7. 2025Deciphering Personalization: Towards Fine-Grained Explainability in Natural Language for Personalized Image Generation Modelscited by 1
  8. 2024Efficient Curation of Invertebrate Image Datasets Using Feature Embeddings and Automatic Size Comparisoncited by 0
  9. 2025ImageSet2Text: Describing Sets of Images through Textcited by 0