Domain-specific augmentations and plant-only training data produce stronger self-supervised representations for fine-grained plant recognition than standard SSL pipelines or ImageNet pretraining.
In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)
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This study introduces Predictive Representation Learning (PRL) as a category in self-supervised learning centered on latent prediction of unobserved data, positions JEPA as an example, and reports comparative results for BYOL, MAE, and I-JEPA on similarity and robustness metrics.
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Self-Supervised Learning of Plant Image Representations
Domain-specific augmentations and plant-only training data produce stronger self-supervised representations for fine-grained plant recognition than standard SSL pipelines or ImageNet pretraining.
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From Alignment to Prediction: A Study of Self-Supervised Learning and Predictive Representation Learning
This study introduces Predictive Representation Learning (PRL) as a category in self-supervised learning centered on latent prediction of unobserved data, positions JEPA as an example, and reports comparative results for BYOL, MAE, and I-JEPA on similarity and robustness metrics.