  This work presents a novel training technique for deep neural networks that
makes use of additional data from a distribution that is different from that of
the original input data. This technique aims to reduce overfitting and improve
the generalization performance of the network. Our proposed technique, namely
Passive Batch Injection Training Technique (PBITT), even reduces the level of
overfitting in networks that already use the standard techniques for reducing
overfitting such as $L_2$ regularization and batch normalization, resulting in
significant accuracy improvements. Passive Batch Injection Training Technique
(PBITT) introduces a few passive mini-batches into the training process that
contain data from a distribution that is different from the input data
distribution. This technique does not increase the number of parameters in the
final model and also does not increase the inference (test) time but still
improves the performance of deep CNNs. To the best of our knowledge, this is
the first work that makes use of different data distribution to aid the
training of convolutional neural networks (CNNs). We thoroughly evaluate the
proposed approach on standard architectures: VGG, ResNet, and WideResNet, and
on several popular datasets: CIFAR-10, CIFAR-100, SVHN, and ImageNet. We
observe consistent accuracy improvement by using the proposed technique. We
also show experimentally that the model trained by our technique generalizes
well to other tasks such as object detection on the MS-COCO dataset using
Faster R-CNN. We present extensive ablations to validate the proposed approach.
Our approach improves the accuracy of VGG-16 by a significant margin of 2.1%
over the CIFAR-100 dataset.

Computer Vision and Pattern Recognition;Machine Learning