Benchmarking the Accuracy and Robustness of Feedback Alignment Algorithms
This work provides a tool and benchmarks for researchers in biologically plausible machine learning, but it is incremental as it builds on existing feedback alignment methods.
The authors introduced BioTorch, a software framework for creating, training, and benchmarking biologically motivated neural networks, and conducted a study on feedback alignment methods, revealing the impact of weight initialization and optimizer choice on performance and robustness against adversarial attacks.
Backpropagation is the default algorithm for training deep neural networks due to its simplicity, efficiency and high convergence rate. However, its requirements make it impossible to be implemented in a human brain. In recent years, more biologically plausible learning methods have been proposed. Some of these methods can match backpropagation accuracy, and simultaneously provide other extra benefits such as faster training on specialized hardware (e.g., ASICs) or higher robustness against adversarial attacks. While the interest in the field is growing, there is a necessity for open-source libraries and toolkits to foster research and benchmark algorithms. In this paper, we present BioTorch, a software framework to create, train, and benchmark biologically motivated neural networks. In addition, we investigate the performance of several feedback alignment methods proposed in the literature, thereby unveiling the importance of the forward and backward weight initialization and optimizer choice. Finally, we provide a novel robustness study of these methods against state-of-the-art white and black-box adversarial attacks.