# The Herbarium Challenge 2019 Dataset

arXiv: https://arxiv.org/abs/1906.05372 · HTML: https://mlsanity.com/paper/1906.05372/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 30 of 60)

- [Generalized Category Discovery](https://mlsanity.com/paper/2201.02609/) (2022)
- [Parametric Classification for Generalized Category Discovery: A Baseline Study](https://mlsanity.com/paper/2211.11727/) (2022)
- [No Representation Rules Them All in Category Discovery](https://mlsanity.com/paper/2311.17055/) (2023)
- [PASS: An ImageNet replacement for self-supervised pretraining without humans](https://mlsanity.com/paper/2109.13228/) (2021)
- [Parametric Information Maximization for Generalized Category Discovery](https://mlsanity.com/paper/2212.00334/) (2022)
- [Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery](https://mlsanity.com/paper/2310.19776/) (2023)
- [Contrastive Mean-Shift Learning for Generalized Category Discovery](https://mlsanity.com/paper/2404.09451/) (2024)
- [OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning](https://mlsanity.com/paper/2207.02261/) (2022)
- [SelEx: Self-Expertise in Fine-Grained Generalized Category Discovery](https://mlsanity.com/paper/2408.14371/) (2024)
- [OpenCon: Open-world Contrastive Learning](https://mlsanity.com/paper/2208.02764/) (2022)
- [Active Generalized Category Discovery](https://mlsanity.com/paper/2403.04272/) (2024)
- [Textual Knowledge Matters: Cross-Modality Co-Teaching for Generalized Visual Class Discovery](https://mlsanity.com/paper/2403.07369/) (2024)
- [GET: Unlocking the Multi-modal Potential of CLIP for Generalized Category Discovery](https://mlsanity.com/paper/2403.09974/) (2024)
- [CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discovery](https://mlsanity.com/paper/2304.06928/) (2023)
- [Species Distribution Modeling for Machine Learning Practitioners: A Review](https://mlsanity.com/paper/2107.10400/) (2021)
- [Novel Class Discovery for Long-tailed Recognition](https://mlsanity.com/paper/2308.02989/) (2023)
- [ProtoGCD: Unified and Unbiased Prototype Learning for Generalized Category Discovery](https://mlsanity.com/paper/2504.03755/) (2025)
- [DebGCD: Debiased Learning with Distribution Guidance for Generalized Category Discovery](https://mlsanity.com/paper/2504.04804/) (2025)
- [Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement](https://mlsanity.com/paper/2507.06928/) (2025)
- [Dissecting Generalized Category Discovery: Multiplex Consensus under Self-Deconstruction](https://mlsanity.com/paper/2508.10731/) (2025)
- [Large-scale Pre-trained Models are Surprisingly Strong in Incremental Novel Class Discovery](https://mlsanity.com/paper/2303.15975/) (2023)
- [Flipped Classroom: Aligning Teacher Attention with Student in Generalized Category Discovery](https://mlsanity.com/paper/2409.19659/) (2024)
- [SEAL: Semantic-Aware Hierarchical Learning for Generalized Category Discovery](https://mlsanity.com/paper/2510.18740/) (2025)
- [Contextuality Helps Representation Learning for Generalized Category Discovery](https://mlsanity.com/paper/2407.19752/) (2024)
- [Generalized Category Discovery via Reciprocal Learning and Class-Wise Distribution Regularization](https://mlsanity.com/paper/2506.02334/) (2025)
- [Consistent Supervised-Unsupervised Alignment for Generalized Category Discovery](https://mlsanity.com/paper/2507.04725/) (2025)
- [The Power of Transfer Learning in Agricultural Applications: AgriNet](https://mlsanity.com/paper/2207.03881/) (2022)
- [Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual Classification](https://mlsanity.com/paper/2410.04492/) (2024)
- [Category Discovery: An Open-World Perspective](https://mlsanity.com/paper/2509.22542/) (2025)
- [Generalized Category Discovery with Clustering Assignment Consistency](https://mlsanity.com/paper/2310.19210/) (2023)

## Abstract (arXiv)

Herbarium sheets are invaluable for botanical research, and considerable time and effort is spent by experts to label and identify specimens on them. In view of recent advances in computer vision and deep learning, developing an automated approach to help experts identify specimens could significantly accelerate research in this area. Whereas most existing botanical datasets comprise photos of specimens in the wild, herbarium sheets exhibit dried specimens, which poses new challenges. We present a challenge dataset of herbarium sheet images labeled by experts, with the intent of facilitating the development of automated identification techniques for this challenging scenario.