# The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/the-unreasonable-effectiveness-of-noisy-data-for-fine-grained-recognition/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Jonathan Krause,Benjamin Sapp,Andrew Howard,Howard Zhou,Alexander Toshev,Tom Duerig,James Philbin,Li Fei-Fei |
| Citations | 304 |
| DOI | 10.1007/978-3-319-46487-9_19 |
| Fields | Computer Science |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W2287418003 |
| Type | conference-paper |
| Year | 2016 |

## Paper authors

- [Jonathan Krause](https://scholariq.org/researchers/jonathan-krause/)
- [Li Fei-Fei](https://scholariq.org/researchers/li-fei-fei/)

## Paper journal

- [Lecture notes in computer science](https://scholariq.org/journals/lecture-notes-in-computer-science/)

## Paper primary topic

- [Machine Learning and Algorithms](https://scholariq.org/topics/machine-learning-and-algorithms/)

## Paper topics

- [Machine Learning and Algorithms](https://scholariq.org/topics/machine-learning-and-algorithms/)
- [Advanced Image and Video Retrieval Techniques](https://scholariq.org/topics/advanced-image-and-video-retrieval-techniques/)
- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.