Abstract
Out-of-distribution detection ensures trustworthiness in machine learning systems by detecting anomalous data points and adjusting confidence in predictions accordingly. However, another key use-case of out-of-distribution detection is the assessment of data quality with respect to a desired distribution or semantic range of data. This work proposes a simple but powerful approach that allows for cleaning of image data based on descriptively defining desired data as well as undesired data. Notably, this method does not require the training of a machine learning model. In addition, this work presents a new image dataset suited for evaluating data cleaning tasks in a way that has practical relevance, and demonstrates satisfactory experimental results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Google Image Scraper repository: https://github.com/ohyicong/Google-Image-Scraper.
- 2.
- 3.
References
Dai, Y., Lang, H., Zeng, K., Huang, F., Li, Y.: Exploring large language models for multi-modal out-of-distribution detection. In: Bouamor, H., Pino, J., Bali, K. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 5292–5305. Association for Computational Linguistics, Singapore (2023). https://doi.org/10.18653/v1/2023.findings-emnlp.351, https://aclanthology.org/2023.findings-emnlp.351
Esmaeilpour, S., Liu, B., Robertson, E., Shu, L.: Zero-shot open set detection by extending CLIP. CoRR abs/2109.02748 (2021). https://arxiv.org/abs/2109.02748
Fort, S., Ren, J., Lakshminarayanan, B.: Exploring the limits of out-of-distribution detection. CoRR abs/2106.03004 (2021). https://arxiv.org/abs/2106.03004
Hsu, Y., Shen, Y., Jin, H., Kira, Z.: Generalized ODIN: detecting out-of-distribution image without learning from out-of-distribution data. CoRR abs/2002.11297 (2020). https://arxiv.org/abs/2002.11297
Krizhevsky, A.: Learning multiple layers of features from tiny images. University of Toronto (2012)
McNemar, Q.: Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2), 153–157 (1947). https://doi.org/10.1007/bf02295996
Menon, S., Vondrick, C.: Visual classification via description from large language models. In: The Eleventh International Conference on Learning Representations (2023). https://openreview.net/forum?id=jlAjNL8z5cs
Ming, Y., Cai, Z., Gu, J., Sun, Y., Li, W., Li, Y.: Delving into out-of-distribution detection with vision-language representations (2022)
Radford, A., et al.: Learning transferable visual models from natural language supervision. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18–24 July 2021, Virtual Event. Proceedings of Machine Learning Research, vol. 139, pp. 8748–8763. PMLR (2021). http://proceedings.mlr.press/v139/radford21a.html
Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30–44 (2019). https://doi.org/10.1016/j.media.2019.01.010, https://www.sciencedirect.com/science/article/pii/S1361841518302640
Yang, J., Zhou, K., Li, Y., Liu, Z.: Generalized out-of-distribution detection: a survey. CoRR abs/2110.11334 (2021). https://arxiv.org/abs/2110.11334
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kharma, S., Großmann, J. (2024). Image Dataset Quality Assessment Through Descriptive Out-of-Distribution Detection. In: Hotho, A., Rudolph, S. (eds) KI 2024: Advances in Artificial Intelligence. KI 2024. Lecture Notes in Computer Science(), vol 14992 . Springer, Cham. https://doi.org/10.1007/978-3-031-70893-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-70893-0_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-70892-3
Online ISBN: 978-3-031-70893-0
eBook Packages: Computer ScienceComputer Science (R0)