SINDER: Repairing the Singular Defects of DINOv2
<|reference_start|>SINDER: Repairing the Singular Defects of DINOv2: Vision Transformer models trained on large-scale datasets, although effective, often exhibit artifacts in the patch token they extract. While such defects can be alleviated by re-training the entire model with additional classification tokens, the underlying reasons for the presence of these tokens remain unclear. In this paper, we conduct a thorough investigation of this phenomenon, combining theoretical analysis with empirical observations. Our findings reveal that these artifacts originate from the pre-trained network itself, specifically stemming from the leading left singular vector of the network's weights. Furthermore, to mitigate these defects, we propose a novel fine-tuning smooth regularization that rectifies structural deficiencies using only a small dataset, thereby avoiding the need for complete re-training. We validate our method on various downstream tasks, including unsupervised segmentation, classification, supervised segmentation, and depth estimation, demonstrating its effectiveness in improving model performance. Codes and checkpoints are available at https://github.com/haoqiwang/sinder.<|reference_end|>
@article{wang2024sinder:,
    title={SINDER: Repairing the Singular Defects of DINOv2},
    author={Haoqi Wang, Tong Zhang, Mathieu Salzmann},
    journal={arXiv preprint arXiv:2407.16826},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16826},
    primaryClass={cs.CV}
}