How many views does your deep neural network use for prediction?
<|reference_start|>How many views does your deep neural network use for prediction?: The generalization ability of Deep Neural Networks (DNNs) is still not fully understood, despite numerous theoretical and empirical analyses. Recently, Allen-Zhu & Li (2023) introduced the concept of multi-views to explain the generalization ability of DNNs, but their main target is ensemble or distilled models, and no method for estimating multi-views used in a prediction of a specific input is discussed. In this paper, we propose Minimal Sufficient Views (MSVs), which is similar to multi-views but can be efficiently computed for real images. MSVs is a set of minimal and distinct features in an input, each of which preserves a model's prediction for the input. We empirically show that there is a clear relationship between the number of MSVs and prediction accuracy across models, including convolutional and transformer models, suggesting that a multi-view like perspective is also important for understanding the generalization ability of (non-ensemble or non-distilled) DNNs.<|reference_end|>
@article{kawano2024how,
    title={How many views does your deep neural network use for prediction?},
    author={Keisuke Kawano and Takuro Kutsuna and Keisuke Sano},
    journal={arXiv preprint arXiv:2402.01095},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01095},
    primaryClass={cs.LG cs.AI cs.CV stat.ML}
}