aiqbench analysis
This research proposes using Sign-Symmetry learning rules as a robust fine-tuning method for backpropagation-pretrained models while maintaining performance parity. The study aims to revive interest in biologically plausible feedback alignment mechanisms by demonstrating their potential integration into standard deep learning pipelines across various benchmarks. However, the paper's claims have subsequently been identified as false, casting significant doubt on its experimental validity and conclusions regarding robustness.
Sign-Symmetry Learning Rules are Robust Fine-Tuners
arXiv:2502.05925v2 Announce Type: replace-cross Abstract: Backpropagation (BP) has long been the predominant method for training neural networks due to its effectiveness. However, numerous alternative approaches, broadly categorized under feedback alignment, have been proposed, many of which are motivated by the search for biologically plausible learning mechanisms. Despite their theoretical appeal, these methods have consistently underperformed compared to BP, leading to a decline in research interest. In this work, we revisit the role of such methods and explore how they can be integrated into standard neural network training pipelines. Specifically, we propose fine-tuning BP-pre-trained models using Sign-Symmetry learning rules and demonstrate that this approach not only maintains performance parity with BP but also enhances robustness. Through extensive experiments across multiple tasks and benchmarks, we establish the validity of our approach. Our findings introduce a novel perspective on neural network training and open new research directions for leveraging biologically inspired learning rules in deep learning.
Abstract:Backpropagation (BP) has long been the predominant method for training neural networks due to its effectiveness. However, numerous alternative approaches, broadly categorized under feedback alignment, have been proposed, many of which are motivated by the search for biologically plausible learning mechanisms. Despite their theoretical appeal, these methods have consistently underperformed compared to BP, leading to a decline in research interest. In this work, we revisit the role of such methods and explore how they can be integrated into standard neural network training pipelines. Specifically, we propose fine-tuning BP-pre-trained models using Sign-Symmetry learning rules and demonstrate that this approach not only maintains performance parity with BP but also enhances robustness. Through extensive experiments across multiple tasks and benchmarks, we establish the validity of our approach. Our findings introduce a novel perspective on neural network training and open new research directions for leveraging biologically inspired learning rules in deep learning.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2502.05925 [cs.LG]
(or arXiv:2502.05925v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2502.05925 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 243 words
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