This paper tackles the problem of how to pre-train a model and make it
generally reusable backbones for downstream task learning. In pre-training, we
propose a method that builds an agent-environment interaction model by learning
domain invariant successor features from the agent's vast experiences covering
various tasks, then discretize them into behavior prototypes which result in an
embodied set structure. To make the model generally reusable for downstream
task learning, we propose (1) embodied feature projection that retains previous
knowledge by projecting the new task's observation-action pair to the embodied
set structure and (2) projected Bellman updates which add learning plasticity
for the new task setting. We provide preliminary results that show downstream
task learning based on a pre-trained embodied set structure can handle unseen
changes in task objectives, environmental dynamics and sensor modalities.

本篇论文研究了如何预训练模型，并使其成为下游任务学习的可重复使用的通用架构。我们提出了一种构建代理 - 环境交互模型的方法，通过学习代理在各种任务中获得的域不变的后继特征并将其离散化为行为原型，建立了一个带有身体结构的模型。为了使模型成为下游任务学习的通用架构，我们提出了两种方法：（1）身体特征投影，通过将新任务的观察 - 动作对映射到身体结构中保留以前的知识；（2）投影贝尔曼更新，在新任务环境中增加学习可塑性。我们的初步研究结果表明，基于经过预训练的身体结构的下游任务学习可以处理任务目标、环境动态和传感器模式中的未知变化。

构建通用可重复使用的智能体 - 环境交互模型

Build generally reusable agent-environment interaction models

Self-supervised learning approaches leverage unlabeled samples to acquire
generic knowledge about different concepts, hence allowing for
annotation-efficient downstream task learning. In this paper, we propose a
novel self-supervised method that leverages multiple imaging modalities. We
introduce the multimodal puzzle task, which facilitates rich representation
learning from multiple image modalities. The learned representations allow for
subsequent fine-tuning on different downstream tasks. To achieve that, we learn
a modality-agnostic feature embedding by confusing image modalities at the
data-level. Together with the Sinkhorn operator, with which we formulate the
puzzle solving optimization as permutation matrix inference instead of
classification, they allow for efficient solving of multimodal puzzles with
varying levels of complexity. In addition, we also propose to utilize
cross-modal generation techniques for multimodal data augmentation used for
training self-supervised tasks. In other words, we exploit synthetic images for
self-supervised pretraining, instead of downstream tasks directly, in order to
circumvent quality issues associated with synthetic images, while improving
data-efficiency and representations quality. Our experimental results, which
assess the gains in downstream performance and data-efficiency, show that
solving our multimodal puzzles yields better semantic representations, compared
to treating each modality independently. Our results also highlight the
benefits of exploiting synthetic images for self-supervised pretraining. We
showcase our approach on four downstream tasks: Brain tumor segmentation and
survival days prediction using four MRI modalities, Prostate segmentation using
two MRI modalities, and Liver segmentation using unregistered CT and MRI
modalities. We outperform many previous solutions, and achieve results
competitive to state-of-the-art.

该论文提出了一种利用多个成像模式的新型自监督方法，设计了一个多模式拼图任务以促进多种图像模式的特征表征，提出了一种利用跨模态生成技术进行多模态数据增强的方法，实验表明该方法能够提高数据效率和语义表征，广泛应用于四种不同的下游任务，并取得了领先的成果。