  We propose Curriculum by Masking (CBM), a novel state-of-the-art curriculum
learning strategy that effectively creates an easy-to-hard training schedule
via patch (token) masking, offering significant accuracy improvements over the
conventional training regime and previous curriculum learning (CL) methods. CBM
leverages gradient magnitudes to prioritize the masking of salient image
regions via a novel masking algorithm and a novel masking block. Our approach
enables controlling sample difficulty via the patch masking ratio, generating
an effective easy-to-hard curriculum by gradually introducing harder samples as
training progresses. CBM operates with two easily configurable parameters, i.e.
the number of patches and the curriculum schedule, making it a versatile
curriculum learning approach for object recognition and detection. We conduct
experiments with various neural architectures, ranging from convolutional
networks to vision transformers, on five benchmark data sets (CIFAR-10,
CIFAR-100, ImageNet, Food-101 and PASCAL VOC), to compare CBM with conventional
as well as curriculum-based training regimes. Our results reveal the
superiority of our strategy compared with the state-of-the-art curriculum
learning regimes. We also observe improvements in transfer learning contexts,
where CBM surpasses previous work by considerable margins in terms of accuracy.
We release our code for free non-commercial use at
https://github.com/CroitoruAlin/CBM.
