  Deep learning models learn to fit training data while they are highly
expected to generalize well to testing data. Most works aim at finding such
models by creatively designing architectures and fine-tuning parameters. To
adapt to particular tasks, hand-crafted information such as image prior has
also been incorporated into end-to-end learning. However, very little progress
has been made on investigating how an individual training sample will influence
the generalization ability of a model. In other words, to achieve high
generalization accuracy, do we really need all the samples in a training
dataset? In this paper, we demonstrate that deep learning models such as
convolutional neural networks may not favor all training samples, and
generalization accuracy can be further improved by dropping those unfavorable
samples. Specifically, the influence of removing a training sample is
quantifiable, and we propose a Two-Round Training approach, aiming to achieve
higher generalization accuracy. We locate unfavorable samples after the first
round of training, and then retrain the model from scratch with the reduced
training dataset in the second round. Since our approach is essentially
different from fine-tuning or further training, the computational cost should
not be a concern. Our extensive experimental results indicate that, with
identical settings, the proposed approach can boost performance of the
well-known networks on both high-level computer vision problems such as image
classification, and low-level vision problems such as image denoising.
