Representation learning is a fundamental task in machine learning, aiming at
uncovering structures from data to facilitate subsequent tasks. However, what
is a good representation for planning and reasoning in a stochastic world
remains an open problem. In this work, we posit that learning a distance
function is essential to allow planning and reasoning in the representation
space. We show that a geometric abstraction of the probabilistic world dynamics
can be embedded into the representation space through asymmetric contrastive
learning. Unlike previous approaches that focus on learning mutual similarity
or compatibility measures, we instead learn an asymmetric similarity function
that reflects the state reachability and allows multi-way probabilistic
inference. Moreover, by conditioning on a common reference state (e.g. the
observer's current state), the learned representation space allows us to
discover the geometrically salient states that only a handful of paths can lead
through. These states can naturally serve as subgoals to break down
long-horizon planning tasks. We evaluate our method in gridworld environments
with various layouts and demonstrate its effectiveness in discovering the
subgoals.

学习一个距离函数对计划和推理在表示空间中至关重要，通过非对称对比学习将概率世界动态嵌入到表示空间中，并通过一个共同参考状态，发现几何上显著的状态作为子目标，有效地发现子目标。

具有非对称距离度量的概率世界建模

Probabilistic World Modeling with Asymmetric Distance Measure

Contrastive learning, which is a powerful technique for learning image-level
representations from unlabeled data, leads a promising direction to dealing
with the dilemma between large-scale pre-training and limited labeled data.
However, most existing contrastive learning strategies are designed mainly for
downstream tasks of natural images, therefore they are sub-optimal and even
worse than learning from scratch when directly applied to medical images whose
downstream tasks are usually segmentation. In this work, we propose a novel
asymmetric contrastive learning framework named JCL for medical image
segmentation with self-supervised pre-training. Specifically, (1) A novel
asymmetric contrastive learning strategy is proposed to pre-train both encoder
and decoder simultaneously in one-stage to provide better initialization for
segmentation models. (2) A multi-level contrastive loss is designed to take the
correspondence among feature-level, image-level and pixel-level projections,
respectively into account to make sure multi-level representations can be
learned by the encoder and decoder during pre-training. (3) Experiments on
multiple medical image datasets indicate our JCL framework outperforms existing
SOTA contrastive learning strategies.

提出了一种名为 JCL 的新型不对称对比学习框架，用于医学图像分割的自监督预训练，多级对比损失设计确保了编码器和解码器在预训练期间可以学习到多级表示，实验证明该框架优于现有的对比学习策略。