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

arXiv: https://arxiv.org/abs/1511.06789 · HTML: https://mlsanity.com/paper/1511.06789/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2015.

## Built on by (most cited first, 30 of 48)

- [Benchmarking Representation Learning for Natural World Image Collections](https://mlsanity.com/paper/2103.16483/) (2021)
- [Fine-Grained Image Analysis with Deep Learning: A Survey](https://mlsanity.com/paper/2111.06119/) (2021)
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- [Webly Supervised Concept Expansion for General Purpose Vision Models](https://mlsanity.com/paper/2202.02317/) (2022)
- [Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification](https://mlsanity.com/paper/2101.06635/) (2021)
- [Danish Fungi 2020 -- Not Just Another Image Recognition Dataset](https://mlsanity.com/paper/2103.10107/) (2021)
- [Improving Fractal Pre-training](https://mlsanity.com/paper/2110.03091/) (2021)
- [PDiscoNet: Semantically consistent part discovery for fine-grained recognition](https://mlsanity.com/paper/2309.03173/) (2023)
- [The iNaturalist Sounds Dataset](https://mlsanity.com/paper/2506.00343/) (2025)
- [An Instance-Dependent Simulation Framework for Learning with Label Noise](https://mlsanity.com/paper/2107.11413/) (2021)
- [Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset](https://mlsanity.com/paper/2207.10664/) (2022)
- [Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanism](https://mlsanity.com/paper/2208.00617/) (2022)
- [Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition](https://mlsanity.com/paper/2102.09875/) (2021)
- [A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation](https://mlsanity.com/paper/2303.09165/) (2023)
- [EfficientCLIP: Efficient Cross-Modal Pre-training by Ensemble Confident Learning and Language Modeling](https://mlsanity.com/paper/2109.04699/) (2021)
- [The Curious Layperson: Fine-Grained Image Recognition without Expert Labels](https://mlsanity.com/paper/2111.03651/) (2021)
- [PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck](https://mlsanity.com/paper/2403.05297/) (2024)
- [Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification](https://mlsanity.com/paper/2307.15427/) (2023)
- [From Categories to Classifiers: Name-Only Continual Learning by Exploring the Web](https://mlsanity.com/paper/2311.11293/) (2023)
- [PLReMix: Combating Noisy Labels with Pseudo-Label Relaxed Contrastive Representation Learning](https://mlsanity.com/paper/2402.17589/) (2024)
- [Multiplicative Reweighting for Robust Neural Network Optimization](https://mlsanity.com/paper/2102.12192/) (2021)
- [Fine-Grained Adversarial Semi-supervised Learning](https://mlsanity.com/paper/2110.05848/) (2021)
- [Combining Deep Learning and Street View Imagery to Map Smallholder Crop Types](https://mlsanity.com/paper/2309.05930/) (2023)
- [Learning with Noisy Labels: Interconnection of Two Expectation-Maximizations](https://mlsanity.com/paper/2401.04390/) (2024)
- [Active Labeling: Streaming Stochastic Gradients](https://mlsanity.com/paper/2205.13255/) (2022)
- [Reduction of Class Activation Uncertainty with Background Information](https://mlsanity.com/paper/2305.03238/) (2023)
- [CAPro: Webly Supervised Learning with Cross-Modality Aligned Prototypes](https://mlsanity.com/paper/2310.09761/) (2023)
- [Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise](https://mlsanity.com/paper/2412.00150/) (2024)
- [Learning from Crowds with Sparse and Imbalanced Annotations](https://mlsanity.com/paper/2107.05039/) (2021)
- [Towards Robust Cross-domain Image Understanding with Unsupervised Noise Removal](https://mlsanity.com/paper/2109.04284/) (2021)

## Abstract (arXiv)

Current approaches for fine-grained recognition do the following: First, recruit experts to annotate a dataset of images, optionally also collecting more structured data in the form of part annotations and bounding boxes. Second, train a model utilizing this data. Toward the goal of solving fine-grained recognition, we introduce an alternative approach, leveraging free, noisy data from the web and simple, generic methods of recognition. This approach has benefits in both performance and scalability. We demonstrate its efficacy on four fine-grained datasets, greatly exceeding existing state of the art without the manual collection of even a single label, and furthermore show first results at scaling to more than 10,000 fine-grained categories. Quantitatively, we achieve top-1 accuracies of 92.3% on CUB-200-2011, 85.4% on Birdsnap, 93.4% on FGVC-Aircraft, and 80.8% on Stanford Dogs without using their annotated training sets. We compare our approach to an active learning approach for expanding fine-grained datasets.