  Convolutional Neural Networks (CNN) have demon- strated its successful
applications in computer vision, speech recognition, and natural language
processing. For object recog- nition, CNNs might be limited by its strict label
requirement and an implicit assumption that images are supposed to be target-
object-dominated for optimal solutions. However, the labeling procedure,
necessitating laying out the locations of target ob- jects, is very tedious,
making high-quality large-scale dataset prohibitively expensive. Data
augmentation schemes are widely used when deep networks suffer the insufficient
training data problem. All the images produced through data augmentation share
the same label, which may be problematic since not all data augmentation
methods are label-preserving. In this paper, we propose a weakly supervised CNN
framework named Multiple Instance Learning Convolutional Neural Networks
(MILCNN) to solve this problem. We apply MILCNN framework to object recognition
and report state-of-the-art performance on three benchmark datasets: CIFAR10,
CIFAR100 and ILSVRC2015 classification dataset.
