Residual Connections Harm Abstract Feature Learning in Masked Autoencoders
<|reference_start|>Residual Connections Harm Abstract Feature Learning in Masked Autoencoders: We demonstrate that adding a weighting factor to decay the strength of identity shortcuts within residual networks substantially improves semantic feature learning in the state-of-the-art self-supervised masked autoencoding (MAE) paradigm. Our modification to the identity shortcuts within a VIT-B/16 backbone of an MAE boosts linear probing accuracy on ImageNet from 67.8% to 72.7%. This significant gap suggests that, while residual connection structure serves an essential role in facilitating gradient propagation, it may have a harmful side effect of reducing capacity for abstract learning by virtue of injecting an echo of shallower representations into deeper layers. We ameliorate this downside via a fixed formula for monotonically decreasing the contribution of identity connections as layer depth increases. Our design promotes the gradual development of feature abstractions, without impacting network trainability. Analyzing the representations learned by our modified residual networks, we find correlation between low effective feature rank and downstream task performance.<|reference_end|>
@article{zhang2024residual,
    title={Residual Connections Harm Generative Representation Learning},
    author={Xiao Zhang, Ruoxi Jiang, William Gao, Rebecca Willett, Michael Maire},
    journal={arXiv preprint arXiv:2404.10947},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10947},
    primaryClass={cs.CV}
}