  Although large-scale labeled data are essential for deep convolutional neural
networks (ConvNets) to learn high-level semantic visual representations, it is
time-consuming and impractical to collect and annotate large-scale datasets. A
simple and efficient unsupervised representation learning method named ScaleNet
based on multi-scale images is proposed in this study to enhance the
performance of ConvNets when limited information is available. The input images
are first resized to a smaller size and fed to the ConvNet to recognize the
rotation degree. Next, the ConvNet learns the rotation-prediction task for the
original size images based on the parameters transferred from the previous
model. The CIFAR-10 and ImageNet datasets are examined on different
architectures such as AlexNet and ResNet50 in this study. The current study
demonstrates that specific image features, such as Harris corner information,
play a critical role in the efficiency of the rotation-prediction task. The
ScaleNet supersedes the RotNet by ~7% in the limited CIFAR-10 dataset. The
transferred parameters from a ScaleNet model with limited data improve the
ImageNet Classification task by about 6% compared to the RotNet model. This
study shows the capability of the ScaleNet method to improve other cutting-edge
models such as SimCLR by learning effective features for classification tasks.
