A review on discriminative self-supervised learning methods
<|reference_start|>A review on discriminative self-supervised learning methods: In the field of computer vision, self-supervised learning has emerged as a method to extract robust features from unlabeled data, where models derive labels autonomously from the data itself, without the need for manual annotation. This paper provides a comprehensive review of discriminative approaches of self-supervised learning within the domain of computer vision, examining their evolution and current status. Through an exploration of various methods including contrastive, self-distillation, knowledge distillation, feature decorrelation, and clustering techniques, we investigate how these approaches leverage the abundance of unlabeled data. Finally, we have comparison of self-supervised learning methods on the standard ImageNet classification benchmark.<|reference_end|>
@article{giakoumoglou2024a,
    title={A review on discriminative self-supervised learning methods},
    author={Nikolaos Giakoumoglou, Tania Stathaki},
    journal={arXiv preprint arXiv:2405.04969},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04969},
    primaryClass={cs.CV cs.AI}
}