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。

ScaleNet：一种有限信息的无监督表示学习方法

ScaleNet: An Unsupervised Representation Learning Method for Limited  Information

Motivated by product detection in supermarkets, this paper studies the
problem of object proposal generation in supermarket images and other natural
images. We argue that estimation of object scales in images is helpful for
generating object proposals, especially for supermarket images where object
scales are usually within a small range. Therefore, we propose to estimate
object scales of images before generating object proposals. The proposed method
for predicting object scales is called ScaleNet. To validate the effectiveness
of ScaleNet, we build three supermarket datasets, two of which are real-world
datasets used for testing and the other one is a synthetic dataset used for
training. In short, we extend the previous state-of-the-art object proposal
methods by adding a scale prediction phase. The resulted method outperforms the
previous state-of-the-art on the supermarket datasets by a large margin. We
also show that the approach works for object proposal on other natural images
and it outperforms the previous state-of-the-art object proposal methods on the
MS COCO dataset. The supermarket datasets, the virtual supermarkets, and the
tools for creating more synthetic datasets will be made public.

为产物察觉问题而深入研究超市图像和其他自然图像中目标提议生成。为了更好地生成目标提议，本文提出了一种名为 ScaleNet 的方法来估算图像中目标的比例，并体现了其有效性。