Self-Balanced Dropout
This addresses a fundamental limitation in dropout for machine learning practitioners, though it appears incremental as it builds directly on existing dropout methods.
The paper tackled the problem of co-adaptation persisting in neural networks despite dropout, by proposing Self-Balanced Dropout, which uses a trainable variable to balance input correlation effects, resulting in significant performance improvements across various tasks.
Dropout is known as an effective way to reduce overfitting via preventing co-adaptations of units. In this paper, we theoretically prove that the co-adaptation problem still exists after using dropout due to the correlations among the inputs. Based on the proof, we further propose Self-Balanced Dropout, a novel dropout method which uses a trainable variable to balance the influence of the input correlation on parameter update. We evaluate Self-Balanced Dropout on a range of tasks with both simple and complex models. The experimental results show that the mechanism can effectively solve the co-adaption problem to some extent and significantly improve the performance on all tasks.