Do Better ImageNet Models Transfer Better?
Open accessAbstract
Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. However, this hypothesis has never been systematically tested. Here, we compare the performance of 16 classification networks on 12 image classification datasets. We find that, when networks are used as fixed feature extractors or fine-tuned, there is a strong correlation between ImageNet accuracy and transfer accuracy (r = 0.99 and 0.96, respectively). In the former setting, we find that this relationship is very sensitive to the way in which networks are trained on ImageNet; many common forms of regularization slightly improve ImageNet accuracy but yield features that are much worse for transfer learning. Additionally, we find that, on two small fine-grained image classification datasets, pretraining on ImageNet provides minimal benefits, indicating the learned features from ImageNet do not transfer well to fine-grained tasks. Together, our results show that ImageNet architectures generalize well across datasets, but ImageNet features are less general than previously suggested.
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Training neural networks with the fine-tune strategy can gain much better results than with random initialization.
“Training neural networks with the fine-tune strategy can gain much better results than with random initialization.”
From A Compact Convolutional Neural Network for Surface Defect Inspection (2020) · cites this paper as [30]
Transferring knowledge from a large scale source dataset to a small one has proved to be a meaningful practice.
“Transferring the knowledge from a large scale source dataset to a small one has proved to be a meaningful practice.”
From A Compact Convolutional Neural Network for Surface Defect Inspection (2020) · cites this paper as [30]
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Bahri, Y., Dyer, E., Kaplan, J., Lee, J., & Sharma, U. (2021) · arXiv · doi:10.48550/arxiv.2102.06701 · Cited by 182
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Kang, M., Song, H., Park, S., Yoo, D., & Pereira, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00326 · Cited by 187 · see citing works
Data Evaluation and Enhancement for Quality Improvement of Machine Learning
Chen, H., Chen, J., & Ding, J. (2021) · IEEE Transactions on Reliability · doi:10.1109/tr.2021.3070863 · Cited by 166 · see citing works
Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
Chen, L.-C., Lopes, R. G., Cheng, B., Collins, M. D., Cubuk, E. D., Zoph, B., Adam, H., & Shlens, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58545-7_40 · Cited by 165 · see citing works
Self-citationAnalysis of the Human Protein Atlas Image Classification competition
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Brain-like functional specialization emerges spontaneously in deep neural networks
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A Compact Convolutional Neural Network for Surface Defect Inspection
Huang, Y., Qiu, C., Wang, X., Wang, S., & Yuan, K. (2020) · Sensors · doi:10.3390/s20071974 · Cited by 85 · see citing works
Subpopulation Data Poisoning Attacks
Jagielski, M., Severi, G., Pousette Harger, N., & Oprea, A. (2021) · Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3460120.3485368 · Cited by 86 · see citing works
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Contrasting Contrastive Self-Supervised Representation Learning Pipelines
Kotar, K., Ilharco, G., Schmidt, L., Ehsani, K., & Mottaghi, R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00980 · Cited by 28 · see citing works
Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations
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A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks
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“DeeSIL is a straightforward application of a transfer learning [24, 40] scheme in an incremental context.”
BackgroundMethod
- Authors
- Kornblith, S., Shlens, J., & Le, Q. V.
- Affiliations
- Google (United States) (US)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 2656-2666
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2804935296
- Cited by
- 1,325
- References
- 87
- Citations by year
- 2026: 106 · 2025: 154 · 2024: 186 · 2023: 170 · 2022: 198 · 2021: 233 · 2020: 167 · 2019: 92 · 2018: 17 · 2017: 1
- FWCI
- 66.00
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- transfer learning, ImageNet, feature extraction, fine-tuning, regularization, pre-training, fine-grained image classification, cross-dataset generalization
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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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