Let Go of Your Labels with Unsupervised Transfer
<|reference_start|>Let Go of Your Labels with Unsupervised Transfer: Foundation vision-language models have enabled remarkable zero-shot transferability of the pre-trained representations to a wide range of downstream tasks. However, to solve a new task, zero-shot transfer still necessitates human guidance to define visual categories that appear in the data. Here, we show that fully unsupervised transfer emerges when searching for the labeling of a dataset that induces maximal margin classifiers in representation spaces of different foundation models. We present TURTLE, a fully unsupervised method that effectively employs this guiding principle to uncover the underlying labeling of a downstream dataset without any supervision and task-specific representation learning. We evaluate TURTLE on a diverse benchmark suite of 26 datasets and show that it achieves new state-of-the-art unsupervised performance. Furthermore, TURTLE, although being fully unsupervised, outperforms zero-shot transfer baselines on a wide range of datasets. In particular, TURTLE matches the average performance of CLIP zero-shot on 26 datasets by employing the same representation space, spanning a wide range of architectures and model sizes. By guiding the search for the underlying labeling using the representation spaces of two foundation models, TURTLE surpasses zero-shot transfer and unsupervised prompt tuning baselines, demonstrating the surprising power and effectiveness of unsupervised transfer.<|reference_end|>
@article{gadetsky2024let,
    title={Let Go of Your Labels with Unsupervised Transfer},
    author={Artyom Gadetsky, Yulun Jiang, Maria Brbic},
    journal={arXiv preprint arXiv:2406.07236},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07236},
    primaryClass={cs.LG}
}