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Synthetic Examples Improve Generalization for Rare Classes
Open accessAbstract
The ability to detect and classify rare occurrences in images has important applications - for example, counting rare and endangered species when studying biodiversity, or detecting infrequent traffic scenarios that pose a danger to self-driving cars. Few-shot learning is an open problem: current computer vision systems struggle to categorize objects they have seen only rarely during training, and collecting a sufficient number of training examples of rare events is often challenging and expensive, and sometimes outright impossible. We explore in depth an approach to this problem: complementing the few available training images with ad-hoc simulated data.Our testbed is animal species classification, which has a real-world long-tailed distribution. We present two natural world simulators, and analyze the effect of different axes of variation in simulation, such as pose, lighting, model, and simulation method, and we prescribe best practices for efficiently incorporating simulated data for real-world performance gain. Our experiments reveal that synthetic data can considerably reduce error rates for classes that are rare, that as the amount of simulated data is increased, accuracy on the target class improves, and that high variation of simulated data provides maximum performance gain.
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“For instance, camera traps take photos automatically whenever a motion sensor is activated—computer vision can be used to classify the species that pass by, supporting a real-time, less labor-intensive species count [64,585,643].”
From Tackling climate change with machine learning (2023) · cites this paper as [64]
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A Proposed Solution to Problems in Learning the Knowledge Needed by Self-Driving VehiclesPreprint
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Optimizing User Experience in Wearable Cognitive Assistance through Model Specialization
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Surveillance robot in hazardous places
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A Novel GAN-Based DDoS Attack Detection Method for Smart Home Networks
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Metadata
- Authors
- Beery, S., Liu, Y., Morris, D., Piavis, J., Kapoor, A., Meister, M., Joshi, N., & Perona, P.
- Affiliations
- California Institute of Technology (US)
- Microsoft (United States) (US)
- Venue
- 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2020-03
- Pages
- 852-862
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W2935725472
- Cited by
- 103
- References
- 78
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 11 · 2023: 15 · 2022: 18 · 2021: 23 · 2020: 11
- FWCI
- 6.39
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1745301)
- U.S. Geological Survey
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- few-shot learning, synthetic data augmentation, animal species classification, long-tailed distributions, endangered species detection
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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