论文标题
课程的自我监督视觉特征学习
Self-supervised visual feature learning with curriculum
论文作者
论文摘要
自我监督的学习技巧表明了他们学习有意义的特征表示的能力。通过训练模型，只需在输入或部分输入之间找到相关性，就可以实现这一点。但是，需要仔细选择此类借口任务，以避免可能使那些借口任务变得微不足道的低级信号。此外，删除这些快捷方式通常会导致一些语义上有价值的信息丢失。我们表明，它直接影响了下游任务的学习速度。在本文中，我们从课程学习中汲取了灵感来逐步删除低级信号，并表明它大大提高了下游任务的收敛速度。
Self-supervised learning techniques have shown their abilities to learn meaningful feature representation. This is made possible by training a model on pretext tasks that only requires to find correlations between inputs or parts of inputs. However, such pretext tasks need to be carefully hand selected to avoid low level signals that could make those pretext tasks trivial. Moreover, removing those shortcuts often leads to the loss of some semantically valuable information. We show that it directly impacts the speed of learning of the downstream task. In this paper we took inspiration from curriculum learning to progressively remove low level signals and show that it significantly increase the speed of convergence of the downstream task.
