  While training on samples drawn from independent and identical distribution
has been a de facto paradigm for optimizing image classification networks,
humans learn new concepts in an easy-to-hard manner and on the selected
examples progressively. Driven by this fact, we investigate the training
paradigms where the samples are not drawn from independent and identical
distribution. We propose a data sampling strategy, named Drop-and-Refresh
(DaR), motivated by the learning behaviors of humans that selectively drop easy
samples and refresh them only periodically. We show in our experiments that the
proposed DaR strategy can maintain (and in many cases improve) the predictive
accuracy even when the training cost is reduced by 15% on various datasets
(CIFAR 10, CIFAR 100 and ImageNet) and with different backbone architectures
(ResNets, DenseNets and MobileNets). Furthermore and perhaps more importantly,
we find the ImageNet pre-trained models using our DaR sampling strategy
achieves better transferability for the downstream tasks including object
detection (+0.3 AP), instance segmentation (+0.3 AP), scene parsing (+0.5 mIoU)
and human pose estimation (+0.6 AP). Our investigation encourages people to
rethink the connections between the sampling strategy for training and the
transferability of its learned features for pre-training ImageNet models.
