Current vision systems are trained on huge datasets, and these datasets come
with costs: curation is expensive, they inherit human biases, and there are
concerns over privacy and usage rights. To counter these costs, interest has
surged in learning from cheaper data sources, such as unlabeled images. In this
paper we go a step further and ask if we can do away with real image datasets
entirely, instead learning from noise processes. We investigate a suite of
image generation models that produce images from simple random processes. These
are then used as training data for a visual representation learner with a
contrastive loss. We study two types of noise processes, statistical image
models and deep generative models under different random initializations. Our
findings show that it is important for the noise to capture certain structural
properties of real data but that good performance can be achieved even with
processes that are far from realistic. We also find that diversity is a key
property to learn good representations. Datasets, models, and code are
available at this https URL

该研究使用不同的噪声过程生成图像，并将其用作视觉表示学习者的训练数据。通过使用对比损失，研究两种类型的噪声过程，发现噪声需要捕捉真实数据的某些结构性质，但即使与真实数据差异很大的过程也可以实现良好的性能。多样性是学习良好表示的关键属性。

通过观察噪声学习视觉

Learning to See by Looking at Noise

Unsupervised domain adaptation (UDA) is the task of modifying a statistical
model trained on labeled data from a source domain to achieve better
performance on data from a target domain, with access to only unlabeled data in
the target domain. Existing state-of-the-art UDA approaches use neural networks
to learn representations that can predict the values of subset of important
features called "pivot features." In this work, we show that it is possible to
improve on these methods by jointly training the representation learner with
the task learner, and examine the importance of existing pivot selection
methods.

本文研究了无监督领域自适应的方法，使用神经网络学习表示来预测重要特征的子集，并探讨了联合训练表示和任务学习者、现有枢轴选择方法的重要性。