Is Meta-training Really Necessary for Molecular Few-Shot Learning ?
<|reference_start|>Is Meta-training Really Necessary for Molecular Few-Shot Learning ?: Few-shot learning has recently attracted significant interest in drug discovery, with a recent, fast-growing literature mostly involving convoluted meta-learning strategies. We revisit the more straightforward fine-tuning approach for molecular data, and propose a regularized quadratic-probe loss based on the the Mahalanobis distance. We design a dedicated block-coordinate descent optimizer, which avoid the degenerate solutions of our loss. Interestingly, our simple fine-tuning approach achieves highly competitive performances in comparison to state-of-the-art methods, while being applicable to black-box settings and removing the need for specific episodic pre-training strategies. Furthermore, we introduce a new benchmark to assess the robustness of the competing methods to domain shifts. In this setting, our fine-tuning baseline obtains consistently better results than meta-learning methods.<|reference_end|>
@article{formont2024is,
    title={Is Meta-training Really Necessary for Molecular Few-Shot Learning ?},
    author={Philippe Formont, Hugo Jeannin, Pablo Piantanida, Ismail Ben Ayed},
    journal={arXiv preprint arXiv:2404.02314},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02314},
    primaryClass={cs.LG cs.AI}
}