Home › Entities › academia › Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches

Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches

Research article (2020 25th International Conference on Pattern Recognition (ICPR), 2021) · cited 11× · AI/ML
academia openalex:W3163046659
Press Enter · cited answer in seconds

Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches

Summary

Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches is a scholarly article[1].

Key Facts

  • Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches's instance of is recorded as scholarly article[2].

Related Entities

Margret Keuper

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

📑 Cite this page

Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches. Retrieved May 24, 2026, from https://4ort.xyz/entity/learning-embeddings-for-image-clustering-an-empirical-study-of-triplet-loss-approaches
MLA “Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/learning-embeddings-for-image-clustering-an-empirical-study-of-triplet-loss-approaches.
BibTeX @misc{4ortxyz_learning-embeddings-for-image-clustering-an-empirical-study-of-triplet-loss-approaches_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches}}, year = {2026}, url = {https://4ort.xyz/entity/learning-embeddings-for-image-clustering-an-empirical-study-of-triplet-loss-approaches}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches — https://4ort.xyz/entity/learning-embeddings-for-image-clustering-an-empirical-study-of-triplet-loss-approaches (retrieved 2026-05-24)

Canonical URL: https://4ort.xyz/entity/learning-embeddings-for-image-clustering-an-empirical-study-of-triplet-loss-approaches · Last refreshed: