Carpe Diem, Seize the Samples Uncertain "At the Moment" for Adaptive Batch Selection
This addresses the challenge of efficient and accurate training for deep learning practitioners, offering incremental improvements over existing batch selection methods.
The paper tackles the problem of improving deep neural network training by proposing Recency Bias, an adaptive batch selection algorithm that selects uncertain samples based on recent prediction inconsistencies, resulting in up to 20.97% reduction in test error and up to 59.32% faster training time.
The accuracy of deep neural networks is significantly affected by how well mini-batches are constructed during the training step. In this paper, we propose a novel adaptive batch selection algorithm called Recency Bias that exploits the uncertain samples predicted inconsistently in recent iterations. The historical label predictions of each training sample are used to evaluate its predictive uncertainty within a sliding window. Then, the sampling probability for the next mini-batch is assigned to each training sample in proportion to its predictive uncertainty. By taking advantage of this design, Recency Bias not only accelerates the training step but also achieves a more accurate network. We demonstrate the superiority of Recency Bias by extensive evaluation on two independent tasks. Compared with existing batch selection methods, the results showed that Recency Bias reduced the test error by up to 20.97% in a fixed wall-clock training time. At the same time, it improved the training time by up to 59.32% to reach the same test error