The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Boosting Self-Supervised Learning via Knowledge Transfer
Open accessAbstract
In self-supervised learning, one trains a model to solve a so-called pretext task on a dataset without the need for human annotation. The main objective, however, is to transfer this model to a target domain and task. Currently, the most effective transfer strategy is fine-tuning, which restricts one to use the same model or parts thereof for both pretext and target tasks. In this paper, we present a novel framework for self-supervised learning that overcomes limitations in designing and comparing different tasks, models, and data domains. In particular, our framework decouples the structure of the self-supervised model from the final task-specific fine-tuned model. This allows us to: 1) quantitatively assess previously incompatible models including handcrafted features; 2) show that deeper neural network models can learn better representations from the same pretext task; 3) transfer knowledge learned with a deep model to a shallower one and thus boost its learning. We use this framework to design a novel self-supervised task, which achieves state-of-the-art performance on the common benchmarks in PASCAL VOC 2007, ILSVRC12 and Places by a significant margin. Our learned features shrink the mAP gap between models trained via self-supervised learning and supervised learning from 5.9% to 2.6% in object detection on PASCAL VOC 2007.
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Citations
Cites
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Emerging Properties in Self-Supervised Vision Transformers
Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00951 · Cited by 5,638
“Previous works have also combined self-supervised learning and knowledge distillation, enabling self-supervised model compression [20] and performance gains [12, 38].”
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Self-Supervised Learning of Pretext-Invariant Representations
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Self-Supervised Learning in Remote Sensing: A review
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Unsupervised Pre-Training of Image Features on Non-Curated Data
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A Survey on Semi-, Self- and Unsupervised Learning for Image Classification
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3D Human Action Representation Learning via Cross-View Consistency Pursuit
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A Cookbook of Self-Supervised Learning
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“This strategy was superseded by “jigsaw” methods [Pathak et al., 2016, Noroozi et al., 2018] that break an image into an array of disjoint patches and predict the relative location of each.”
MethodSelf-citationSelf-supervised learning for videos: A survey
Schiappa, M. C., Rawat, Y. S., & Shah, M. (2023) · ACM Computing Surveys · doi:10.1145/3577925 · Cited by 155
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods
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Large-Scale Unsupervised Semantic Segmentation
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Modeling Quality of IoT Experience in Autonomous Vehicles
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Deep Self-Supervised Representation Learning for Free-Hand Sketch
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LT-GAN: Self-Supervised GAN with Latent Transformation Detection
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View Enhanced Jigsaw Puzzle for Self-Supervised Feature Learning in 3D Human Action Recognition
You, W., & Wang, X. (2022) · IEEE Access · doi:10.1109/access.2022.3165040 · Cited by 6
Cornelissen, S., van der Putten, J. A., Boers, T. G. W., Jukema, J. B., Fockens, K. N., Bergman, J. J. G. H. M., van der Sommen, F., & de With, P. H. N. (2021) · 2021 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip42928.2021.9506121 · Cited by 2
Metadata
- Authors
- Noroozi, M., Vinjimoor, A., Favaro, P., & Pirsiavash, H.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 9359-9367
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2799113232
- Cited by
- 296
- References
- 10
- FWCI
- 17.69
- Type
- conference paper
- Language
- en
- Open access
- green
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