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.

本研究提出了一种名为 ScaleNet 的简单高效的无监督表示学习方法，通过使用多尺度图像来增强有限信息条件下卷积神经网络 (ConvNets) 的性能，并展示了 ScaleNet 方法在旋转预测任务上的优越性及其对其他模型的分类任务的改进能力。