Fine-grained Classes and How to Find Them
<|reference_start|>Fine-grained Classes and How to Find Them: In many practical applications, coarse-grained labels are readily available compared to fine-grained labels that reflect subtle differences between classes. However, existing methods cannot leverage coarse labels to infer fine-grained labels in an unsupervised manner. To bridge this gap, we propose FALCON, a method that discovers fine-grained classes from coarsely labeled data without any supervision at the fine-grained level. FALCON simultaneously infers unknown fine-grained classes and underlying relationships between coarse and fine-grained classes. Moreover, FALCON is a modular method that can effectively learn from multiple datasets labeled with different strategies. We evaluate FALCON on eight image classification tasks and a single-cell classification task. FALCON outperforms baselines by a large margin, achieving 22% improvement over the best baseline on the tieredImageNet dataset with over 600 fine-grained classes.<|reference_end|>
@article{grcić2024fine-grained,
    title={Fine-grained Classes and How to Find Them},
    author={Matej Grci'c, Artyom Gadetsky, Maria Brbi'c},
    journal={arXiv preprint arXiv:2406.11070},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11070},
    primaryClass={cs.LG cs.CV}
}