With the limited availability of labeled data with various atmospheric
conditions in remote sensing images, it seems useful to work with
self-supervised algorithms. Few pretext-based algorithms, including from
rotation, spatial context and jigsaw puzzles are not appropriate for satellite
images. Often, satellite images have a higher temporal frequency. So, the
temporal dimension of remote sensing data provides natural augmentation without
requiring us to create artificial augmentation of images. Here, we propose
S3-TSS, a novel method of self-supervised learning technique that leverages
natural augmentation occurring in temporal dimension. We compare our results
with current state-of-the-art methods and also perform various experiments. We
observed that our method was able to perform better than baseline SeCo in four
downstream datasets. Code for our work can be found here:
this https URL

提出了一种名为 S3-TSS 的自监督学习技术方法，利用时序维度中自然增强的遥感数据进行训练，在四个下游数据集中表现优于基准算法 SeCo。

卫星图像的时间自监督（S3-TSS）：一种卫星图像中的 SSL 技术的新方法

Self-Supervision in Time for Satellite Images(S3-TSS): A novel method of  SSL technique in Satellite images

Deep learning methods have become the state of the art for undersampled MR
reconstruction. Particularly for cases where it is infeasible or impossible for
ground truth, fully sampled data to be acquired, self-supervised machine
learning methods for reconstruction are becoming increasingly used. However
potential issues in the validation of such methods, as well as their
generalizability, remain underexplored. In this paper, we investigate important
aspects of the validation of self-supervised algorithms for reconstruction of
undersampled MR images: quantitative evaluation of prospective reconstructions,
potential differences between prospective and retrospective reconstructions,
suitability of commonly used quantitative metrics, and generalizability. Two
self-supervised algorithms based on self-supervised denoising and the deep
image prior were investigated. These methods are compared to a least squares
fitting and a compressed sensing reconstruction using in-vivo and phantom data.
Their generalizability was tested with prospectively under-sampled data from
experimental conditions different to the training. We show that prospective
reconstructions can exhibit significant distortion relative to retrospective
reconstructions/ground truth. Furthermore, pixel-wise quantitative metrics may
not capture differences in perceptual quality accurately, in contrast to a
perceptual metric. In addition, all methods showed potential for
generalization; however, generalizability is more affected by changes in
anatomy/contrast than other changes. We further showed that no-reference image
metrics correspond well with human rating of image quality for studying
generalizability. Finally, we showed that a well-tuned compressed sensing
reconstruction and learned denoising perform similarly on all data.

本文深入探讨自监督学习算法在 MRI 图像重建中的验证方法，发现定量评估前瞻性重建、常用度量标准的适用性以及泛化能力等方面有重要影响，最终验证得到压缩感知重建和学习去噪方法的表现相似。

自监督图像重建方法在欠采样 MRI 中的验证及泛化能力

Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI

Recently, contrastive learning has largely advanced the progress of
unsupervised visual representation learning. Pre-trained on ImageNet, some
self-supervised algorithms reported higher transfer learning performance
compared to fully-supervised methods, seeming to deliver the message that human
labels hardly contribute to learning transferrable visual features. In this
paper, we defend the usefulness of semantic labels but point out that
fully-supervised and self-supervised methods are pursuing different kinds of
features. To alleviate this issue, we present a new algorithm named Supervised
Contrastive Adjustment in Neighborhood (SCAN) that maximally prevents the
semantic guidance from damaging the appearance feature embedding. In a series
of downstream tasks, SCAN achieves superior performance compared to previous
fully-supervised and self-supervised methods, and sometimes the gain is
significant. More importantly, our study reveals that semantic labels are
useful in assisting self-supervised methods, opening a new direction for the
community.

本文提出了一种名为 SCAN 的新算法，该算法通过最大限度地防止语义指导损坏外观特征嵌入，有效的融合了有监督和自监督学习方法，取得了比以往的有监督和自监督方法更好的性能，并揭示了语义标签在辅助自监督方法中的作用。