Fine-Grained Representation Learning via Multi-Level Contrastive Learning without Class Priors
<|reference_start|>Fine-Grained Representation Learning via Multi-Level Contrastive Learning without Class Priors: Recent advances in unsupervised representation learning often rely on knowing the number of classes to improve feature extraction and clustering. However, this assumption raises an important question: is the number of classes always necessary, and do class labels fully capture the fine-grained features within the data? In this paper, we propose Contrastive Disentangling (CD), a framework designed to learn representations without relying on class priors. CD leverages a multi-level contrastive learning strategy, integrating instance-level and feature-level contrastive losses with a normalized entropy loss to capture semantically rich and fine-grained representations. Specifically, (1) the instance-level contrastive loss separates feature representations across samples; (2) the feature-level contrastive loss promotes independence among feature heads; and (3) the normalized entropy loss ensures feature diversity and prevents feature collapse. Extensive experiments on CIFAR-10, CIFAR-100, STL-10, and ImageNet-10 demonstrate that CD outperforms existing methods in scenarios where class information is unavailable or ambiguous. The code is available at https://github.com/Hoper-J/Contrastive-Disentangling.<|reference_end|>
@article{jiang2024fine-grained,
    title={Fine-Grained Representation Learning via Multi-Level Contrastive
  Learning without Class Priors},
    author={Houwang Jiang, Zhuxian Liu, Guodong Liu, Xiaolong Liu, Shihua Zhan},
    journal={arXiv preprint arXiv:2409.04867},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04867},
    primaryClass={cs.CV}
}